Alumni

2018

  • P. S. Vokuda, “Interactive Object Detection,” Master Thesis, Grantham-Allee 20, 53757 St. Augustin, Germany, 2018.
    [BibTeX] [Abstract]

    The success of state-of-the-art object detection methods depend heavily on the availability of a large amount of annotated image data. The raw image data available from various sources are abundant but non-annotated. Annotating image data is often costly, time-consuming or needs expert help. In this work, a new paradigm of learning called Active Learning is explored which uses user interaction to obtain annotations for a sub- set of the dataset. The goal of active learning is to achieve superior object detection performance with images that are annotated on demand. To realize active learning method, the trade-off between the effort to annotate (annotation cost) unlabelled data and the performance of object detection model is minimised. Random Forests based method called Hough Forest is chosen as the object detection model and the annotation cost is calculated as the predicted false positive and false negative rate. The framework is successfully evaluated on two Computer Vision benchmark and two Carl Zeiss custom datasets. Also, an evaluation of RGB, HoG and Deep features for the task is presented. Experimental results show that using Deep features with Hough Forest achieves the maximum performance. By employing Active Learning, it is demonstrated that performance comparable to the fully supervised setting can be achieved by annotating just 2.5\% of the dataset. To this end, an annotation tool is developed for user interaction during Active Learning.

    @MastersThesis{ 2018vokuda,
    abstract  = {The success of state-of-the-art object detection methods
    depend heavily on the availability of a large amount of
    annotated image data. The raw image data available from
    various sources are abundant but non-annotated. Annotating
    image data is often costly, time-consuming or needs expert
    help. In this work, a new paradigm of learning called
    Active Learning is explored which uses user interaction to
    obtain annotations for a sub- set of the dataset. The goal
    of active learning is to achieve superior object detection
    performance with images that are annotated on demand. To
    realize active learning method, the trade-off between the
    effort to annotate (annotation cost) unlabelled data and
    the performance of object detection model is minimised.
    Random Forests based method called Hough Forest is chosen
    as the object detection model and the annotation cost is
    calculated as the predicted false positive and false
    negative rate. The framework is successfully evaluated on
    two Computer Vision benchmark and two Carl Zeiss custom
    datasets. Also, an evaluation of RGB, HoG and Deep features
    for the task is presented.
    Experimental results show that using Deep features with
    Hough Forest achieves the maximum performance. By employing
    Active Learning, it is demonstrated that performance
    comparable to the fully supervised setting can be achieved
    by annotating just 2.5\% of the dataset. To this end, an
    annotation tool is developed for user interaction during
    Active Learning.},
    address  = {Grantham-Allee 20, 53757 St. Augustin, Germany},
    annote  = {WS15 FH-BRS - Pl{\"o}ger, Thiele supervising},
    author  = {Priyanka Subramanya Vokuda},
    month = {April},
    school  = {Bonn-Rhein-Sieg University of Applied Sciences},
    title = {Interactive Object Detection},
    year = {2018}
    }

  • M. Naazare, “Simultaneous Exploration and Information Delivery Using Multiple Heterogeneous Robots Under Limited Communication,” Master Thesis, Grantham-Allee 20, 53757 St. Augustin, Germany, 2018.
    [BibTeX] [Abstract]

    After a disaster, there is an urgent need to deliver humanitarian aid such as emergency medical treatment, supply of food and medicines to the affected regions. Since disasters can cause existing maps to change, reaching the affected regions immediately can be extremely challenging. In this work, the problem of exploring an unknown environment using a team of Unmanned Aerial Vehicles (UAVs) and Unmanned Ground Vehicles (UGV) to discover traversable regions for emergency response vehicles is investigated. The goal of the exploration is to ensure that the explored information reaches the base station despite its strict limited communication range which allows no inter-robot communication outside the range. For the given exploration scenario, the key issue addressed is the coordination of multiple-robots The proposed Simultaneous Exploration and Information Delivery (SEAID) planner operates as a centralised structure and assigns target positions to the Multi-robot System (MRS) to explore, and report the gathered information periodically. It deploys UAVs to perform Boustrophedon motions while UGVs perform frontier-based exploration. As UAVs return periodically, more information about the environment is revealed and the planner switches from frontier-based planning to grid-based coverage for the UGVs where it assigns grid-like pattern of target positions. To ensure coordination, the planner predicts the influence of other robots due to their presence and their respective goals on the new information that can be gained for a robot. Additionally, it employs a divide and rule strategy and assigns robots their respective regions to carry out their tasks. To evaluate the performance of different coordination strategies and observe the behaviour of the MRS, a Robot Operating System-based benchmark called SEAID-Sim was developed to simulate the given scenario. The proposed SEAID planner was tested against existing techniques for multi-robot exploration and multi-robot coverage. The results demonstrate that SEAID planner explores the complete environment, discovers all the obstacles, traversable and non-traversable regions for the emergency response vehicles faster than existing techniques.

    @MastersThesis{ 2018naazare,
    abstract  = {After a disaster, there is an urgent need to deliver
    humanitarian aid such as emergency medical treatment,
    supply of food and medicines to the affected regions. Since
    disasters can cause existing maps to change, reaching the
    affected regions immediately can be extremely challenging.
    In this work, the problem of exploring an unknown
    environment using a team of Unmanned Aerial Vehicles (UAVs)
    and Unmanned Ground Vehicles (UGV) to discover traversable
    regions for emergency response vehicles is investigated.
    The goal of the exploration is to ensure that the explored
    information reaches the base station despite its strict
    limited communication range which allows no inter-robot
    communication outside the range. For the given exploration
    scenario, the key issue addressed is the coordination of
    multiple-robots
    The proposed Simultaneous Exploration and Information
    Delivery (SEAID) planner operates as a centralised
    structure and assigns target positions to the Multi-robot
    System (MRS) to explore, and report the gathered
    information periodically. It deploys UAVs to perform
    Boustrophedon motions while UGVs perform frontier-based
    exploration. As UAVs return periodically, more information
    about the environment is revealed and the planner switches
    from frontier-based planning to grid-based coverage for the
    UGVs where it assigns grid-like pattern of target
    positions. To ensure coordination, the planner predicts the
    influence of other robots due to their presence and their
    respective goals on the new information that can be gained
    for a robot. Additionally, it employs a divide and rule
    strategy and assigns robots their respective regions to
    carry out their tasks.
    To evaluate the performance of different coordination
    strategies and observe the behaviour of the MRS, a Robot
    Operating System-based benchmark called SEAID-Sim was
    developed to simulate the given scenario. The proposed
    SEAID planner was tested against existing techniques for
    multi-robot exploration and multi-robot coverage. The
    results demonstrate that SEAID planner explores the
    complete environment, discovers all the obstacles,
    traversable and non-traversable regions for the emergency
    response vehicles faster than existing techniques.},
    address  = {Grantham-Allee 20, 53757 St. Augustin, Germany},
    annote  = {[SS 2014] [Becker], [Alda], [Br{\"u}ggemann]},
    author  = {Menaka Naazare},
    month = {June},
    school  = {Bonn-Rhein-Sieg University of Applied Sciences},
    title = {Simultaneous Exploration and Information Delivery Using
    Multiple Heterogeneous Robots Under Limited Communication},
    year = {2018}
    }

  • L. O. A. Camargo, “Artificial Transfer Learning in Convolutional Neural Networks,” Master Thesis, Grantham-Allee 20, 53757 St. Augustin, Germany, 2018.
    [BibTeX] [Abstract]

    Recent studies in transfer learning with synthetic data have shown significant results in the context of deep neural networks. However, most of these developments have not been thoroughly studied for the tasks of object detection and image classification. To research the learning transfer ability of artificial neural networks for these specific tasks, we generated over 1M synthetic images. These images were rendered from 35K 3D models extracted from the ShapeNet dataset. We trained several convolutional neural network architectures to perform classification and object detection using our synthetic datasets. We then performed transfer learning by fine-tuning the networks with real images extracted from the COCO and VOC2007/2012 datasets. In order to provide statistically significant results, we trained over 1K CNNs and we show that networks pre-trained with synthetic data are able to obtain higher accuracies and mAPs than the networks trained from scratch. Thus, we show empirical evidence that inexpensive synthetic data can be used to improve general classification and detection tasks in deep learning models. Furthermore, we are able to leverage from this result in order to train a real-time CPU object detector under the single-shot multibox detector scheme. Finally we also provide a self-contained deep learning review that allows us to revisit several hypothesis made in modern CNN architectures.

    @MastersThesis{ 2018arriaga,
    abstract  = {Recent studies in transfer learning with synthetic data
    have shown significant results in the context of deep
    neural networks. However, most of these developments have
    not been thoroughly studied for the tasks of object
    detection and image classification. To research the
    learning transfer ability of artificial neural networks for
    these specific tasks, we generated over 1M synthetic
    images. These images were rendered from 35K 3D models
    extracted from the ShapeNet dataset. We trained several
    convolutional neural network architectures to perform
    classification and object detection using our synthetic
    datasets. We then performed transfer learning by
    fine-tuning the networks with real images extracted from
    the COCO and VOC2007/2012 datasets. In order to provide
    statistically significant results, we trained over 1K CNNs
    and we show that networks pre-trained with synthetic data
    are able to obtain higher accuracies and mAPs than the
    networks trained from scratch. Thus, we show empirical
    evidence that inexpensive synthetic data can be used to
    improve general classification and detection tasks in deep
    learning models. Furthermore, we are able to leverage from
    this result in order to train a real-time CPU object
    detector under the single-shot multibox detector scheme.
    Finally we also provide a self-contained deep learning
    review that allows us to revisit several hypothesis made in
    modern CNN architectures.},
    address  = {Grantham-Allee 20, 53757 St. Augustin, Germany},
    annote  = {WS15/16 Pl{\"o}ger, Asteroth, Valdenegro-Toro supervising},
    author  = {Luis Octavio Arriaga Camargo},
    month = {February},
    school  = {Bonn-Rhein-Sieg University of Applied Sciences},
    title = {Artificial Transfer Learning in Convolutional Neural
    Networks},
    year = {2018}
    }

  • A. Ajmera, “Estimation of Prediction Uncertainty for Semantic Scene Labeling Using Bayesian Approximation,” Master Thesis, Grantham-Allee 20, 53757 St. Augustin, Germany, 2018.
    [BibTeX] [Abstract]

    With the advancement in technology, autonomous and assisted driving are close to being reality. A key component of such systems is the understanding of the surrounding environment. This understanding about the environment can be attained by performing semantic labeling of the driving scenes. Existing deep learning based models have been developed over the years that outperform classical image processing algorithms for the task of semantic labeling. However, the existing models only produce semantic predictions and do not provide a measure of uncertainty about the predictions. Hence, this work focuses on developing a deep learning based semantic labeling model that can produce semantic predictions and their corresponding uncertainties. Autonomous driving needs a real-time operating model, however the Full Resolution Residual Network (FRRN) [4] architecture, which is found as the best performing architecture during literature search, is not able to satisfy this condition. Hence, a small network, similar to FRRN, has been developed and used in this work. Based on the work of [13], the developed network is then extended by adding dropout layers and the dropouts are used during testing to perform approximate Bayesian inference. The existing works on uncertainties, do not have quantitative metrics to evaluate the quality of uncertainties estimated by a model. Hence, the area under curve (AUC) of the receiver operating characteristic (ROC) curves is proposed and used as an evaluation metric in this work. Further, a comparative analysis about the influence of dropout layer position, drop probability and the number of samples, on the quality of uncertainty estimation is performed. Finally, based on the insights gained from the analysis, a model with optimal configuration of dropout is developed. It is then evaluated on the Cityscape dataset and shown to be outperforming the baseline model with an AUC-ROC of about 90%, while the latter having AUC-ROC of about 80%.

    @MastersThesis{ 2018ajmera,
    abstract  = {With the advancement in technology, autonomous and
    assisted driving are close to being reality. A key
    component of such systems is the understanding of the
    surrounding environment. This understanding about the
    environment can be attained by performing semantic labeling
    of the driving scenes. Existing deep learning based models
    have been developed over the years that outperform
    classical image processing algorithms for the task of
    semantic labeling. However, the existing models only
    produce semantic predictions and do not provide a measure
    of uncertainty about the predictions. Hence, this work
    focuses on developing a deep learning based semantic
    labeling model that can produce semantic predictions and
    their corresponding uncertainties.
    Autonomous driving needs a real-time operating model,
    however the Full Resolution Residual Network (FRRN) [4]
    architecture, which is found as the best performing
    architecture during literature search, is not able to
    satisfy this condition. Hence, a small network, similar to
    FRRN, has been developed and used in this work. Based on
    the work of [13], the developed network is then extended by
    adding dropout layers and the dropouts are used during
    testing to perform approximate Bayesian inference. The
    existing works on uncertainties, do not have quantitative
    metrics to evaluate the quality of uncertainties estimated
    by a model. Hence, the area under curve (AUC) of the
    receiver operating characteristic (ROC) curves is proposed
    and used as an evaluation metric in this work. Further, a
    comparative analysis about the influence of dropout layer
    position, drop probability and the number of samples, on
    the quality of uncertainty estimation is performed.
    Finally, based on the insights gained from the analysis, a
    model with optimal configuration of dropout is developed.
    It is then evaluated on the Cityscape dataset and shown to
    be outperforming the baseline model with an AUC-ROC of
    about 90%, while the latter having AUC-ROC of about 80%.},
    address  = {Grantham-Allee 20, 53757 St. Augustin, Germany},
    annote  = {WS15/16, Fraunhofer IAIS Ploeger, Herpers, Eickeler
    supervising},
    author  = {Anand Ajmera},
    month = {January},
    school  = {Bonn-Rhein-Sieg University of Applied Sciences},
    title = {Estimation of Prediction Uncertainty for Semantic Scene
    Labeling Using Bayesian Approximation},
    year = {2018}
    }

2017

  • P. Lukin, “Comparison of controller auto-tuning methods for manipulator axis,” mathesis Master Thesis, Grantham-Allee 20, 53757 St. Augustin, Germany, 2017.
    [BibTeX] [Abstract]

    Robotic arms have a variety of applications in industry and can be programmed to perform specific tasks. Control process of robotic arms is usually reduced to end-effector trajectory following. This task requires position control of all manipulator joints. The current standard control algorithm is PID controller and various modifications of it. Performance of the controller is governed by a set of parameters that are specified during the tuning process. These parameters can drastically change behavior of the system and must be chosen correctly in order to meet demanded controller performance. There are numerous tuning methods that were designed to achieve different control-loop qualities. Additionally, a control engineer must be involved every time a robot is deployed. Thus, it is important to investigate which methods can be successfully applied to tune robotic arm joints in an automatic manner. The main goal of this thesis is to investigate what controller auto-tuning method is the most efficient for manipulator axis position control. The object of the research is the robotic arm axis that includes a joint with an attached load and a control unit. Efficiency of the controller is based on closed-loop control specifications: transient response, robustness and disturbance rejection. The approach is to design a mathematical model of the manipulator axis and obtain model parameters using system identification methods. Next, linearization and model order reduction are applied to the obtained model that is used for comparative analysis of existing PID-based controllers and tuning methods. The most theoretically promising algorithms are applied for controller design given control-loop specification. MATLAB Simulink batch experiments with pseudo-random manipulator parameters performed to evaluate tuning algorithms. Finally, the most effective auto-tuning method is validated on board of BLDC motor yielding good results and concurrence with theoretical expectations.

    @MastersThesis{ 2017lukin,
    author  = {Petr Lukin},
    title = {Comparison of controller auto-tuning methods for
    manipulator axis},
    school  = {Hochschule Bonn-Rhein-Sieg},
    year = {2017},
    type = {mathesis},
    address  = {Grantham-Allee 20, 53757 St. Augustin, Germany},
    month = nov,
    abstract  = {Robotic arms have a variety of applications in industry
    and can be programmed to perform specific tasks. Control
    process of robotic arms is usually reduced to end-effector
    trajectory following. This task requires position control
    of all manipulator joints. The current standard control
    algorithm is PID controller and various modifications of
    it. Performance of the controller is governed by a set of
    parameters that are specified during the tuning process.
    These parameters can drastically change behavior of the
    system and must be chosen correctly in order to meet
    demanded controller performance. There are numerous tuning
    methods that were designed to achieve different
    control-loop qualities. Additionally, a control engineer
    must be involved every time a robot is deployed. Thus, it
    is important to investigate which methods can be
    successfully applied to tune robotic arm joints in an
    automatic manner.
    The main goal of this thesis is to investigate what
    controller auto-tuning method is the most efficient for
    manipulator axis position control. The object of the
    research is the robotic arm axis that includes a joint with
    an attached load and a control unit. Efficiency of the
    controller is based on closed-loop control specifications:
    transient response, robustness and disturbance rejection.
    The approach is to design a mathematical model of the
    manipulator axis and obtain model parameters using system
    identification methods. Next, linearization and model order
    reduction are applied to the obtained model that is used
    for comparative analysis of existing PID-based controllers
    and tuning methods. The most theoretically promising
    algorithms are applied for controller design given
    control-loop specification. MATLAB Simulink batch
    experiments with pseudo-random manipulator parameters
    performed to evaluate tuning algorithms. Finally, the most
    effective auto-tuning method is validated on board of BLDC
    motor yielding good results and concurrence with
    theoretical expectations. },
    annote  = {WS15/16 Synapticon GmbH, Pl{\"o}ger, Chakirov supervising}
    }

  • C. Quignon, “Simultaneous Estimation of Rewards and Dynamics in Continuous Environments,” Master Thesis, Grantham-Allee 20, 53757 St. Augustin, Germany, 2017.
    [BibTeX] [Abstract]

    The field of robotics is steadily progressing into more diverse settings and increasingly complex tasks. Often, the exact environment that a robot will be applied in is not known nor is it possible to accurately specify solutions to the desired tasks. Instead of defining a rule based solution, Inverse Reinforcement Learning (IRL) provides an approach to learn a task from expert demonstrations by estimating the reward function of a Markov Decision Process. This reward function can be used to infer a policy solving the task even in new environments. The current state of the art in research often assumes the dynamics of the environment to be given. This assumption can hardly be satisfied in real world problems. This thesis introduces a novel approach to simultaneously estimate the reward function and the dynamics from a limited set of expert demonstrations in continuous state and action spaces. The approach is compared to Relative Entropy IRL (REIRL) with a naive dynamics estimate. The experimental evaluation showed that the simultaneous estimation of the dynamics is more accurate than the naive, fixed estimate. Since both the rewards and the dynamics influence the policy, it is plausible that a more expressive reward function can counteract inaccuracies of the dynamics estimate. To test this, the reward function is modified with additional features. Although both approaches can benefit from additional features, a careful choice of the features and initial values is required. The additional degree of freedom from the added features may result in an a worse dynamics estimate, overfitting or an ambiguous reward function. A theoretic explanation on the latter is also provided.

    @MastersThesis{ 2017quignon,
    abstract  = {The field of robotics is steadily progressing into more
    diverse settings and increasingly complex tasks. Often, the
    exact environment that a robot will be applied in is not
    known nor is it possible to accurately specify solutions to
    the desired tasks. Instead of defining a rule based
    solution, Inverse Reinforcement Learning (IRL) provides an
    approach to learn a task from expert demonstrations by
    estimating the reward function of a Markov Decision
    Process. This reward function can be used to infer a policy
    solving the task even in new environments. The current
    state of the art in research often assumes the dynamics of
    the environment to be given. This assumption can hardly be
    satisfied in real world problems.
    This thesis introduces a novel approach to simultaneously
    estimate the reward function and the dynamics from a
    limited set of expert demonstrations in continuous state
    and action spaces. The approach is compared to Relative
    Entropy IRL (REIRL) with a naive dynamics estimate. The
    experimental evaluation showed that the simultaneous
    estimation of the dynamics is more accurate than the naive,
    fixed estimate. Since both the rewards and the dynamics
    influence the policy, it is plausible that a more
    expressive reward function can counteract inaccuracies of
    the dynamics estimate. To test this, the reward function is
    modified with additional features. Although both approaches
    can benefit from additional features, a careful choice of
    the features and initial values is required. The additional
    degree of freedom from the added features may result in an
    a worse dynamics estimate, overfitting or an ambiguous
    reward function. A theoretic explanation on the latter is
    also provided.},
    address  = {Grantham-Allee 20, 53757 St. Augustin, Germany},
    annote  = {SS14 Robert Bosch GmbH Herman, Kraetzschmar, M{\"u}ller,
    supervising},
    author  = {Christophe Quignon},
    month = {February},
    school  = {Bonn-Rhein-Sieg University of Applied Sciences},
    title = {Simultaneous Estimation of Rewards and Dynamics in
    Continuous Environments},
    year = {2017}
    }

  • M. Schoebel, “Deep Learning for Recognition of Daily-Living Actions,” Master Thesis, Rheinaustraße 32, 53225 Bonn, Germany, 2017.
    [BibTeX] [Abstract]

    With the world’s population growing older and caring facilities having reached their capacities already today, the need for assistive (robotic) systems is specifically critical in elderly care. In order to aid, an autonomous assistive system first needs to know in what form and when help is required. One way to achieve this is to recognize the currently performed actions by a human user and offer assistance accordingly. This work studies \textit{temporal order verification}, a quasi unsupervised pre-training method for improving the recognition of daily living actions from video data, using the \textit{C3D} deep convolutional neural network. In particular, we evaluate \textit{temporal order verification} as a means to incorporate motion sensitivity in 3D convolutional neural networks, which otherwise treat the temporal evolution of a video and the spatial dimension of video frames equally. Since pre-training a network in an unsupervised way does not require labelled data, this method can be specifically beneficial for recognition tasks where large amounts of labelled data are sparse. We focus on the Charades dataset, which features a unique yet small amount of mundane daily-living action videos and therefore enables the design of vision-based systems, which can be deployed in real-world applications such as assistive robotics. Our \textit{C3D} implementation yields an accuracy of $44.57\%$ on the UCF101 standard action recognition benchmark and a mean average precision of $9.01\%$ for recognizing daily-living actions of the Charades dataset, when trained from randomly initialized weights. By pre-training the model with \textit{temporal order verification}, we were not able to improve classification results, in fact the pre-trained weights turned out to impair the network’s performance.

    @MastersThesis{ yyyylastname,
    abstract  = { With the world's population growing older and caring
    facilities having reached their capacities already today,
    the need for assistive (robotic) systems is specifically
    critical in elderly care. In order to aid, an autonomous
    assistive system first needs to know in what form and when
    help is required. One way to achieve this is to recognize
    the currently performed actions by a human user and offer
    assistance accordingly. This work studies \textit{temporal
    order verification}, a quasi unsupervised pre-training
    method for improving the recognition of daily living
    actions from video data, using the \textit{C3D} deep
    convolutional neural network. In particular, we evaluate
    \textit{temporal order verification} as a means to
    incorporate motion sensitivity in 3D convolutional neural
    networks, which otherwise treat the temporal evolution of a
    video and the spatial dimension of video frames equally.
    Since pre-training a network in an unsupervised way does
    not require labelled data, this method can be specifically
    beneficial for recognition tasks where large amounts of
    labelled data are sparse. We focus on the Charades dataset,
    which features a unique yet small amount of mundane
    daily-living action videos and therefore enables the design
    of vision-based systems, which can be deployed in
    real-world applications such as assistive robotics. Our
    \textit{C3D} implementation yields an accuracy of $44.57\%$
    on the UCF101 standard action recognition benchmark and a
    mean average precision of $9.01\%$ for recognizing
    daily-living actions of the Charades dataset, when trained
    from randomly initialized weights. By pre-training the
    model with \textit{temporal order verification}, we were
    not able to improve classification results, in fact the
    pre-trained weights turned out to impair the network's
    performance.},
    address  = {Rheinaustraße 32, 53225 Bonn, Germany},
    annote  = {WS 2015/16, RoboLand Project, Prassler, Plöger
    supervising},
    author  = {Maximilian Schoebel},
    month = {October},
    school  = {Bonn-Rhein-Sieg University of Applied Sciences},
    title = {Deep Learning for Recognition of Daily-Living Actions},
    year = {2017}
    }

  • C. Hebbal, “Developemnt of learning lidar sensor model,” Master Thesis, Grantham-Allee 20, 53757 St. Augustin, Germany, 2017.
    [BibTeX] [Abstract]

    In recent times, academia and industry alike are working in a big way on autonomous vehicles, with the intention of making the future of transportation safe and efficient. Some more time is needed, before we see fully autonomous vehicles on road, but the journey towards it has already begun with the introduction of ADAS and HAD functions in vehicles that enable them to drive around autonomously in selected scenarios like parking, highway driving etc. The key requirement of these systems is comprehensive and accurate information about the surrounding environment of the vehicle. This information is usually represented by maps. There exists numerous approaches to represent environment in literature, among them occupancy grid representation is the popular and most widely used. Occupancy grids tessellate the area to be mapped by means of finite evenly spaced grid of cells. Each cell in the grid is associated with a binary variable that specifies the occupancy value of the cell. Usually to construct maps, occupancy grids make use of inverse sensor models to transform sensor measurements to occupancy values. Most of the existing grid approaches in literature make use of simplified inverse sensor models, which results in generating maps of lower quality. This is due to the fact that most of these models do not take into account the uncertainty in measurement and environmental factors influencing measurement. Thus, in this thesis we set out to develop a better stochastic inverse sensor model by using classical machine learning technique. We developed the stochastic inverse sensor model for the LIDAR sensor by using regression trees. The developed sensor model was evaluated against existing models in simulation and was found to generate better quality maps as compared to existing models. The model was also integrated into EB robinos framework and was evaluated against existing highly tuned static model on measurement data. The quality of map generated by developed model was found to be better than the static model.

    @MastersThesis{ 2017hebbal,
    abstract  = {In recent times, academia and industry alike are working
    in a big way on autonomous vehicles, with the intention of
    making the future of transportation safe and efficient.
    Some more time is needed, before we see fully autonomous
    vehicles on road, but the journey towards it has already
    begun with the introduction of ADAS and HAD functions in
    vehicles that enable them to drive around autonomously in
    selected scenarios like parking, highway driving etc. The
    key requirement of these systems is comprehensive and
    accurate information about the surrounding environment of
    the vehicle. This information is usually represented by
    maps.
    There exists numerous approaches to represent environment
    in literature, among them occupancy grid representation is
    the popular and most widely used. Occupancy grids
    tessellate the area to be mapped by means of finite evenly
    spaced grid of cells. Each cell in the grid is associated
    with a binary variable that specifies the occupancy value
    of the cell. Usually to construct maps, occupancy grids
    make use of inverse sensor models to transform sensor
    measurements to occupancy values. Most of the existing grid
    approaches in literature make use of simplified inverse
    sensor models, which results in generating maps of lower
    quality. This is due to the fact that most of these models
    do not take into account the uncertainty in measurement and
    environmental factors influencing measurement.
    Thus, in this thesis we set out to develop a better
    stochastic inverse sensor model by using classical machine
    learning technique. We developed the stochastic inverse
    sensor model for the LIDAR sensor by using regression
    trees. The developed sensor model was evaluated against
    existing models in simulation and was found to generate
    better quality maps as compared to existing models. The
    model was also integrated into EB robinos framework and was
    evaluated against existing highly tuned static model on
    measurement data. The quality of map generated by developed
    model was found to be better than the static model.},
    address  = {Grantham-Allee 20, 53757 St. Augustin, Germany},
    annote  = {WS 15/16 Elektrobit Automotive Gmbh – EB Robinos
    Pl{\”o}ger, Asterorth, Tollk{\”u}hn supervising},
    author  = {Chaitanya Hebbal},
    month = {December},
    school  = {Bonn-Rhein-Sieg University of Applied Sciences},
    title = {Developemnt of learning lidar sensor model},
    year = {2017}
    }

  • A. Drak, “DoveCopter: A Tracking and Following Aerial Platform for Aerodynamics Analysis of a Velomobile,” Master Thesis, Grantham-Allee 20, 53757 St. Augustin, Germany, 2017.
    [BibTeX] [Abstract]

    Efficient control and design of electrically-assisted vehicles is a well established branch in the field of intelligent transportation which remains under active and constant development. A testbed for such a field is the electrically assisted vehicle “velomobile”. The velomobile has an aerodynamic body fabricated from light weight carbon fiber, two steered front wheels, and an electric motor to assist the rider. Optimization of the body is actively sought and can be achieved through extracting airflow models from tufts attached to the surface of the vehicle. The aim of this project is to provide high quality (HQ) video footage of the tufts under realistic outdoor conditions in order to extract airflow models. Conventionally, airflow models are extracted by means of a wind tunnel or computational fluid dynamics simulation. Woefully however, the former lacks a realistic test environment and the latter comes at a higher computational cost and reduced accuracy. The proposed method to acquire HQ video footage of tufts attached to the velomobile is by means of a drone. The drone will autonomously perform a side-by-side tracking and following of the vehicle while recording HQ video of the tufts. This method has the added benefit of utilizing all the surrounding space to traverse with no limits, thus eliminating the limited recording space restriction caused by side-byside tracking

    @MastersThesis{ 2017drak,
    abstract  = {Efficient control and design of electrically-assisted
    vehicles is a well established branch in the field of
    intelligent transportation which remains under active and
    constant development. A testbed for such a field is the
    electrically assisted vehicle "velomobile". The velomobile
    has an aerodynamic body fabricated from light weight carbon
    fiber, two steered front wheels, and an electric motor to
    assist the rider. Optimization of the body is actively
    sought and can be achieved through extracting airflow
    models from tufts attached to the surface of the vehicle.
    The aim of this project is to provide high quality (HQ)
    video footage of the tufts under realistic outdoor
    conditions in order to extract airflow models.
    Conventionally, airflow models are extracted by means of a
    wind tunnel or computational fluid dynamics simulation.
    Woefully however, the former lacks a realistic test
    environment and the latter comes at a higher computational
    cost and reduced accuracy. The proposed method to acquire
    HQ video footage of tufts attached to the velomobile is by
    means of a drone. The drone will autonomously perform a
    side-by-side tracking and following of the vehicle while
    recording HQ video of the tufts. This method has the added
    benefit of utilizing all the surrounding space to traverse
    with no limits, thus eliminating the limited recording
    space restriction caused by side-byside tracking},
    address  = {Grantham-Allee 20, 53757 St. Augustin, Germany},
    annote  = {[WS2014] [HBRS] [Asteroth], [Julier], [Kruijff]
    supervising},
    author  = {Ahmad Drak},
    month = {July},
    school  = {Bonn-Rhein-Sieg University of Applied Sciences},
    title = {DoveCopter: A Tracking and Following Aerial Platform for
    Aerodynamics Analysis of a Velomobile},
    year = {2017}
    }

  • M. S. Abubucker, “Humanoid Whole-Body Motion Planning for Locomotion Synchronized with Manipulation Task,” Master Thesis, Grantham-Allee 20, 53757 St. Augustin, Germany, 2017.
    [BibTeX] [Abstract]

    In this work, we consider the problem of whole-body motion planning for humanoid that should perform a task by using both the locomotion task and the manipulation task in a synchronized manner. This would allow us to perform smooth reaching motions. The task assigned to the robot would be to reach a goal position with its hand which is quite far away from the robot. In order to reach the goal position, the robot must execute a manipulation task while performing a locomotion task implicitly. The presented whole-body motion planner builds solution by merging CoM movement primitives in the configuration space. The CoM movement primitives are typical humanoid actions like walking with forward step, lateral steps and curved steps. To enable smooth reaching motions, three zones are defined: locomotion zone, loco-manipulation zone and manipulation zone. The planner has been tested for the HRP-4 robot in V-REP simulator. Different types of task were performed with different levels of difficulties to evaluate the planner. The simulation results show that the planner was able to find the solution regardless of the obstacles in the scene and the performance of the planner was evaluated using performance metrics like time taken to plan, tree size and motion duration.

    @MastersThesis{ 2017abubucker,
    abstract  = { In this work, we consider the problem of whole-body
    motion planning for humanoid that should perform a task by
    using both the locomotion task and the manipulation task in
    a synchronized manner. This would allow us to perform
    smooth reaching motions. The task assigned to the robot
    would be to reach a goal position with its hand which is
    quite far away from the robot. In order to reach the goal
    position, the robot must execute a manipulation task while
    performing a locomotion task implicitly. The presented
    whole-body motion planner builds solution by merging CoM
    movement primitives in the configuration space.
    The CoM movement primitives are typical humanoid actions
    like walking with forward step, lateral steps and curved
    steps. To enable smooth reaching motions, three zones are
    defined: locomotion zone, loco-manipulation zone and
    manipulation zone. The planner has been tested for the
    HRP-4 robot in V-REP simulator. Different types of task
    were performed with different levels of difficulties to
    evaluate the planner. The simulation results show that the
    planner was able to find the solution regardless of the
    obstacles in the scene and the performance of the planner
    was evaluated using performance metrics like time taken to
    plan, tree size and motion duration. },
    address  = {Grantham-Allee 20, 53757 St. Augustin, Germany},
    annote  = {SS13 H-BRS , DIAG Sapienza University of Rome - COMANOID
    Pl{\"o}ger, Oriolo, Cognetti supervising},
    author  = {Mohammed Shameer Abubucker},
    month = {December},
    school  = {Bonn-Rhein-Sieg University of Applied Sciences},
    title = {Humanoid Whole-Body Motion Planning for Locomotion
    Synchronized with Manipulation Task},
    year = {2017}
    }

  • D. Arya, “Complete Path Coverage while Exploring Unknown Environment for Radiation Mapping using Unmanned Ground Systems,” Master Thesis, Grantham-Allee 20, 53757 St. Augustin, Germany, 2017.
    [BibTeX] [Abstract]

    In the recent past, several events have highlighted the need for unmanned autonomous systems in disaster situations caused by natural hazards or due to human-driven actions. The use of autonomous systems for the first analysis of the environment and the provision of the initial report can limit hazards to the first responders. This can increase their efficiency in planning response operations and reduce the reaction time. In case of a nuclear disaster, an autonomous system that could gather radiation measurements of the site and provide a radiation map of the area, would be of great help for the response team. Such radiation maps would assist the response team to plan their actions before beginning recovery activities, and thus reduce the risk of putting the life of the recovery team in danger. These radiation maps will also allow them to understand the extent of damage and identify regions where it will be dangerous for the response team to go. In this work, we have developed an online exploration method, Simultaneous Meandering and Mapping (SMAM), for unknown environments which enables a ground robot to cover every free space in the region and gather radiation information. The robot covers this free space effectively without revisiting a space unless it is necessary or unavoidable. Such kind of exploration is called Complete Coverage Path Planning (CCPP). In the proposed algorithm, the robot tries to sweep the region in zig-zag motion and optimizes its path to have maximal straight trajectories. The algorithm stores the environment information regarding obstacles, free spaces and radiation measurement, which is later used to generate a radiation and environment map. We visualized this radiation information as a heat-map laid over the generated environment map and physical layout of the area. We tested SMAM against two existing online CCPP exploration techniques. We chose scenarios with varying complexity from widely open to cluttered environments and analyzed the performance of the three methods in terms of total number of revisits, turns and path length. SMAM has performed better and more efficiently than the other two for complex environments. SMAM can also generate continuous straight trajectories preventing the robot to stop at every position to plan for new goal, and hence save power consumption.

    @MastersThesis{ 2017arya,
    abstract  = {In the recent past, several events have highlighted the
    need for unmanned autonomous systems in disaster situations
    caused by natural hazards or due to human-driven actions.
    The use of autonomous systems for the first analysis of the
    environment and the provision of the initial report can
    limit hazards to the first responders. This can increase
    their efficiency in planning response operations and reduce
    the reaction time. In case of a nuclear disaster, an
    autonomous system that could gather radiation measurements
    of the site and provide a radiation map of the area, would
    be of great help for the response team. Such radiation maps
    would assist the response team to plan their actions before
    beginning recovery activities, and thus reduce the risk of
    putting the life of the recovery team in danger. These
    radiation maps will also allow them to understand the
    extent of damage and identify regions where it will be
    dangerous for the response team to go.
    In this work, we have developed an online exploration
    method, Simultaneous Meandering and Mapping (SMAM), for
    unknown environments which enables a ground robot to cover
    every free space in the region and gather radiation
    information. The robot covers this free space effectively
    without revisiting a space unless it is necessary or
    unavoidable. Such kind of exploration is called Complete
    Coverage Path Planning (CCPP). In the proposed algorithm,
    the robot tries to sweep the region in zig-zag motion and
    optimizes its path to have maximal straight trajectories.
    The algorithm stores the environment information regarding
    obstacles, free spaces and radiation measurement, which is
    later used to generate a radiation and environment map. We
    visualized this radiation information as a heat-map laid
    over the generated environment map and physical layout of
    the area.
    We tested SMAM against two existing online CCPP exploration
    techniques. We chose scenarios with varying complexity from
    widely open to cluttered environments and analyzed the
    performance of the three methods in terms of total number
    of revisits, turns and path length. SMAM has performed
    better and more efficiently than the other two for complex
    environments. SMAM can also generate continuous straight
    trajectories preventing the robot to stop at every position
    to plan for new goal, and hence save power consumption.},
    address  = {Grantham-Allee 20, 53757 St. Augustin, Germany},
    annote  = {WS13/14 Asteroth, M\"uller, Schneider supervising},
    author  = {Devvrat Arya},
    month = {June},
    school  = {Bonn-Rhein-Sieg University of Applied Sciences},
    title = {Complete Path Coverage while Exploring Unknown Environment
    for Radiation Mapping using Unmanned Ground Systems},
    year = {2017}
    }

  • S. Biswas, “Deployment of a Correlation Based Algorithm for a Robotic Black Box,” Master Thesis, Grantham-Allee 20, 53757 St. Augustin, Germany, 2017.
    [BibTeX] [Abstract]

    Robotic systems are vulnerable to faults. If faults in robot systems are not handled quickly enough it might lead to a catastrophe. Hence real-time fault detection and diagnosis on robots are important. Since robots are desired to work autonomously there is usually no human to maintain the robot instantly. This necessitates a fault detection and diagno- sis module on the robot. Again this module itself should not induce fault in the robot system and kept separate from the robot system as much as possible. Hence the idea of a black box is conceived, which would act as an external fault observer to the robot system. Considering the requirement of real-time operation, computationally light ap- proaches should be implemented. Since correlation measure gives hint about how system components might be interdependent on the same system variables, an approach based on correlation is investigated in this work. In order to detect fault the observable sensors and actuators values along with structural model of the system are available to the detection module. This works tries to keep the correlation based approach as general as possible. Finally after investigating deeply, an approach based on correlation change is proposed which is able to detect more faults. Upon detection of fault a simple diagnosis is done based on the knowledge of structural model of the robot system.

    @MastersThesis{ 2008biswas,
    abstract  = {Robotic systems are vulnerable to faults. If faults in
    robot systems are not handled quickly enough it might lead
    to a catastrophe. Hence real-time fault detection and
    diagnosis on robots are important. Since robots are desired
    to work autonomously there is usually no human to maintain
    the robot instantly. This necessitates a fault detection
    and diagno- sis module on the robot. Again this module
    itself should not induce fault in the robot system and kept
    separate from the robot system as much as possible. Hence
    the idea of a black box is conceived, which would act as an
    external fault observer to the robot system. Considering
    the requirement of real-time operation, computationally
    light ap- proaches should be implemented. Since correlation
    measure gives hint about how system components might be
    interdependent on the same system variables, an approach
    based on correlation is investigated in this work. In order
    to detect fault the observable sensors and actuators values
    along with structural model of the system are available to
    the detection module. This works tries to keep the
    correlation based approach as general as possible. Finally
    after investigating deeply, an approach based on
    correlation change is proposed which is able to detect more
    faults. Upon detection of fault a simple diagnosis is done
    based on the knowledge of structural model of the robot
    system.},
    address  = {Grantham-Allee 20, 53757 St. Augustin, Germany},
    annote  = {[SS13] [BRSU] - [Deployment of a Correlation Based
    Algorithm for a Robotic Black Box] Pl{\"o}ger, Breuer,
    Thoduka supervising},
    author  = {Saugata Biswas},
    month = {September},
    school  = {Bonn-Rhein-Sieg University of Applied Sciences},
    title = {Deployment of a Correlation Based Algorithm for a Robotic
    Black Box},
    year = {2017}
    }

2016

  • M. U. Tahir, “Analysis of live 3D mapping approaches with different sensors on various hardware platforms,” Master Thesis, Grantham-Allee 20, 53757 St. Augustin, Germany, 2016.
    [BibTeX] [Abstract]

    SLAM is an active research problem and there are various implementations available under ROS (Robot Operating System) which are real-time but because of low range sensors and hardware limitations, the data quality is not as good as in industrial 3D scanners (e.g. FARO Focus3D scanner) which are not real-time though. So, there is a need to have a system which is real-time and could also deliver high data quality under given circumstances. Certain real-time SLAM approaches may deliver higher data quality under certain environments and system specifications. For this, first an overview of the available algorithms and sensors have been provided followed by their evaluation in terms of sensor trajectory estimation (RMSE) and system performance (CPU + memory). “FARO Laser Tracker Vantage” has been employed to serve as the ground truth for the carried out experiments which has an accuracy of up to 0.015mm. After acquiring and analyzing the results from the experiments it has been concluded that “rtabmap” paired with “Asus Xtion Pro Live” on an Intel NUC board is a better option and is more accurate. The proposed system is real-time, efficient and low cost at the same time as compared to other existing low-cost counter-parts.

    @MastersThesis{ 2016tahir,
    abstract  = {SLAM is an active research problem and there are various
    implementations available under ROS (Robot Operating
    System) which are real-time but because of low range
    sensors and hardware limitations, the data quality is not
    as good as in industrial 3D scanners (e.g. FARO Focus3D
    scanner) which are not real-time though. So, there is a
    need to have a system which is real-time and could also
    deliver high data quality under given circumstances.
    Certain real-time SLAM approaches may deliver higher data
    quality under certain environments and system
    specifications. For this, first an overview of the
    available algorithms and sensors have been provided
    followed by their evaluation in terms of sensor trajectory
    estimation (RMSE) and system performance (CPU + memory).
    "FARO Laser Tracker Vantage" has been employed to serve as
    the ground truth for the carried out experiments which has
    an accuracy of up to 0.015mm. After acquiring and analyzing
    the results from the experiments it has been concluded that
    "rtabmap" paired with "Asus Xtion Pro Live" on an Intel NUC
    board is a better option and is more accurate. The proposed
    system is real-time, efficient and low cost at the same
    time as compared to other existing low-cost
    counter-parts.},
    address  = {Grantham-Allee 20, 53757 St. Augustin, Germany},
    annote  = {WS12/13 FARO Europe - Master Thesis Pl{\"o}ger,
    Kraetzschmar, Zweigle},
    author  = {Muhammad Umair Tahir},
    month = {August},
    school  = {Bonn-Rhein-Sieg University of Applied Sciences},
    title = {Analysis of live 3D mapping approaches with different
    sensors on various hardware platforms},
    year = {2016}
    }

  • I. Siddique, “Comparative Analysis on Sensor Configurations of Autonomous Vehicles for Robust Obstacle Avoidance,” Master Thesis, Grantham-Allee 20, 53757 St. Augustin, Germany, 2016.
    [BibTeX] [Abstract]

    Autonomous vehicle working in close proximity to human raises a safety concern. Such vehicles have to be equipped with flawless obstacle avoidance techniques. Robust obstacle avoidance can only be achieved if the environment could be perceived flawlessly. Unfortunately the sensors to perceive the environment are not flawless. This forced modern day autonomous vehicles to use different combination of sensors to compensate each others limitations. This comes with additional agitation of fusing different sensors data. Besides in spite of combining different sensors there might still be situations which hamper the nominal performance of the sensor configuration. This makes successful and safe deployment of autonomous vehicle in public a very challenging task. Hence a comprehensive survey on sensor configurations is of immense importance. Here in this thesis work different sensors and sensor configurations of recent wheeled autonomous vehicles in indoor and outdoor scenarios have been investigated. The investigated sensor conflagrations were analyzed and compared to find a general trend in these different scenarios. Performance of the individual sensors used in those vehicles were also analyzed and compared.

    @MastersThesis{ 2016siddiqueisnain,
    abstract  = {Autonomous vehicle working in close proximity to human
    raises a safety concern. Such vehicles have to be equipped
    with flawless obstacle avoidance techniques. Robust
    obstacle avoidance can only be achieved if the environment
    could be perceived flawlessly. Unfortunately the sensors to
    perceive the environment are not flawless. This forced
    modern day autonomous vehicles to use different combination
    of sensors to compensate each others limitations. This
    comes with additional agitation of fusing different sensors
    data. Besides in spite of combining different sensors there
    might still be situations which hamper the nominal
    performance of the sensor configuration. This makes
    successful and safe deployment of autonomous vehicle in
    public a very challenging task. Hence a comprehensive
    survey on sensor configurations is of immense importance.
    Here in this thesis work different sensors and sensor
    configurations of recent wheeled autonomous vehicles in
    indoor and outdoor scenarios have been investigated. The
    investigated sensor conflagrations were analyzed and
    compared to find a general trend in these different
    scenarios. Performance of the individual sensors used in
    those vehicles were also analyzed and compared. },
    address  = {Grantham-Allee 20, 53757 St. Augustin, Germany},
    annote  = {WS13/14 FH-HBRS - Comparative Analysis on Sensor
    Configurations of Autonomous Vehicles for Robust Obstacle
    Avoidance, Prassler, Pl{\"o}ger},
    author  = {Isnain Siddique},
    month = {October},
    school  = {Bonn-Rhein-Sieg University of Applied Sciences},
    title = {Comparative Analysis on Sensor Configurations of
    Autonomous Vehicles for Robust Obstacle Avoidance},
    year = {2016}
    }

  • C. K. Tan, “Neural Maps for Robotics – A Biologically-Inspired Approach for Obstacle Avoidance,” Master Thesis, Grantham-Allee 20, 53757 St. Augustin, Germany, 2016.
    [BibTeX] [Abstract]

    A general aim of autonomous robots is to create agents that can operate independently for long periods of time. One key aspect for them will be the control of movement, since a stationary robot would have less utility than a moving one. One way of fulfilling this aim is to build robots such that they mimic the way humans and animals execute movements, since they are extremely versatile and have the ability to learn and generalize their movements to many situations. This would require studies in neuroscience to discover mechanisms behind such adaptability. We propose the use of Dynamic Neural Fields (DNFs) as a tool to build maps of neural activities, which we term “neural maps” in this thesis, to model what is happening within the brain and nervous system during its operation. For example, they can be used as an internal representation of a robot in the context of localization. Another use of such maps is that they are useful in providing a visual representation correlating neural activity and physical activity, aiding understand of what goes on in the neural networks during execution of a task. In our thesis, we extended a generalized motor program from [Stringer et al. 2003] for the task of obstacle avoidance. This program utilizes memorized motor sequences within a neural network for its movement. By adding a decision component into their model, we show that an agent with such a motor program is able to select memorized trajectories that allows it to avoid obstacle while travelling towards a goal. Simulations show that our model is capable of obstacle avoidance. Our extended model can also be viewed in context of a middle-level controller that utilizes simple rules to decide on an action to take, without the intervention from a high-level planner.

    @MastersThesis{ 2016tan,
    abstract  = {A general aim of autonomous robots is to create agents
    that can operate independently for long periods of time.
    One key aspect for them will be the control of movement,
    since a stationary robot would have less utility than a
    moving one. One way of fulfilling this aim is to build
    robots such that they mimic the way humans and animals
    execute movements, since they are extremely versatile and
    have the ability to learn and generalize their movements to
    many situations. This would require studies in neuroscience
    to discover mechanisms behind such adaptability. We propose
    the use of Dynamic Neural Fields (DNFs) as a tool to build
    maps of neural activities, which we term "neural maps" in
    this thesis, to model what is happening within the brain
    and nervous system during its operation. For example, they
    can be used as an internal representation of a robot in the
    context of localization. Another use of such maps is that
    they are useful in providing a visual representation
    correlating neural activity and physical activity, aiding
    understand of what goes on in the neural networks during
    execution of a task. In our thesis, we extended a
    generalized motor program from [Stringer et al. 2003] for
    the task of obstacle avoidance. This program utilizes
    memorized motor sequences within a neural network for its
    movement. By adding a decision component into their model,
    we show that an agent with such a motor program is able to
    select memorized trajectories that allows it to avoid
    obstacle while travelling towards a goal. Simulations show
    that our model is capable of obstacle avoidance. Our
    extended model can also be viewed in context of a
    middle-level controller that utilizes simple rules to
    decide on an action to take, without the intervention from
    a high-level planner.},
    address  = {Grantham-Allee 20, 53757 St. Augustin, Germany},
    annote  = {WS 2013/14, Pl{\"o}ger, Kraetzschmar, Trappenberg},
    author  = {Chun Kwang Tan},
    month = {January},
    school  = {Bonn-Rhein-Sieg University of Applied Sciences},
    title = {Neural Maps for Robotics - A Biologically-Inspired
    Approach for Obstacle Avoidance},
    year = {2016}
    }

  • S. Thoduka, “Motion Detection for Mobile Robots Using Vision,” Master Thesis, Grantham-Allee 20, 53757 St. Augustin, Germany, 2016.
    [BibTeX] [Abstract]

    Motion detection is an important skill for an autonomous mobile robot op- erating in dynamic environments. Motion in the environment could, for instance, indicate the presence of an obstacle, a human attracting the atten- tion of the robot, unwanted disturbance of the environment by the robot etc. Vision-based motion detection is particularly challenging when the robot’s camera is in motion. In addition to detecting independently moving objects while in motion, the robot must be able to distinguish between motions in the environment and motions of its own parts, such as its manipulator. In this thesis, we use a Fourier-Mellin transform-based image registra- tion method to compensate for camera motion before applying two different methods of motion detection: temporal differencing and feature tracking. Self-masking using the robot’s state and model is used to ignore motions of visible robot parts. The approach is evaluated on a set of navigation and manipulation se- quences recorded on a Care-O-bot 3 and a youBot and was also integrated and tested on a real Care-O-bot 3. The image registration method is able to compensate for most of the camera motion, but is still inaccurate at depth discontinuities and when there is large depth variance in the scene. The tem- poral difference method performs better than feature tracking, with a more consistent true positive rate and a lower false discovery rate.

    @MastersThesis{ 2016thoduka,
    abstract  = {Motion detection is an important skill for an autonomous
    mobile robot op- erating in dynamic environments. Motion in
    the environment could, for instance, indicate the presence
    of an obstacle, a human attracting the atten- tion of the
    robot, unwanted disturbance of the environment by the robot
    etc. Vision-based motion detection is particularly
    challenging when the robot’s camera is in motion. In
    addition to detecting independently moving objects while in
    motion, the robot must be able to distinguish between
    motions in the environment and motions of its own parts,
    such as its manipulator. In this thesis, we use a
    Fourier-Mellin transform-based image registra- tion method
    to compensate for camera motion before applying two
    different methods of motion detection: temporal
    differencing and feature tracking. Self-masking using the
    robot’s state and model is used to ignore motions of
    visible robot parts. The approach is evaluated on a set of
    navigation and manipulation se- quences recorded on a
    Care-O-bot 3 and a youBot and was also integrated and
    tested on a real Care-O-bot 3. The image registration
    method is able to compensate for most of the camera motion,
    but is still inaccurate at depth discontinuities and when
    there is large depth variance in the scene. The tem- poral
    difference method performs better than feature tracking,
    with a more consistent true positive rate and a lower false
    discovery rate.},
    address  = {Grantham-Allee 20, 53757 St. Augustin, Germany},
    annote  = {WS13, Pl\"{o}ger, Kraetzschmar, Hegger supervising},
    author  = {Santosh Thoduka},
    month = {September},
    school  = {Bonn-Rhein-Sieg University of Applied Sciences},
    title = {{M}otion {D}etection for {M}obile {R}obots {U}sing
    {V}ision},
    year = {2016}
    }

  • E. Yildiz, P. {G. Ploeger}, S. Alda, and K. Pervoelz, “Fault-Tolerant Software Architecture for the Unmanned Surface Vehicle Roboship Declaration of Authorship,” Master Thesis, Grantham-Allee 20, 53757 St. Augustin, Germany, 2016.
    [BibTeX] [Abstract]

    One of the major goals of research in Robotics is to develop truly autonomous vehicles that can be utilized to assist humans in dangerous environments, especially where human life is under threat. In this regard, Unmanned Surface Vehicles (USVs) are developed and utilized to cope up with military scenarios which put human life at potential risk. One of the very common scenarios is harbor security, where USVs are expected to conduct surveillance and feedback to strengthen the harbor’s security. The (USV) Roboship has been designed and developed for harbor patrolling and coastal surveillance. It goes without saying, in such an application domain dependability plays a major role. USVs must not endanger the mission, face unexpected situations and allow no human intervention in case of a problem. At this point, it should be acknowledged that dependability is strongly coupled with fault tolerance when it comes to autonomous robots. However, due to complicated design schemes of USVs and unpredictable environmental factors of harbor environments, USVs are prone to fail. As it is almost certain that the USVs will fail, in order to not to endanger the mission, USVs should fail in a way that the recovery from the failures is possible, and mission integrity is protected. This paper highlights a fault tolerant software architecture for the USV Roboship. We conduct a comprehensive reliability analysis and propose a fault tolerant scheme, wherein the sensitive points of the existing architecture are spotted, and the fault tolerant techniques are incorporated. Since the approach is model-free and its decisions are made at a high frequency, the system is able to deal with highly dynamic scenarios. We used the UWSim simulation environment to simulate the scenarios in which the USV was supposed to navigate safely using its sensors. Field tests have proven the performance and reliability of the system to be satisfactory, yielding 53.57% decrease in faults. We plan to deploy the USV in Eckernfoerde harbor region of Germany to test the scheme in the future.

    @MastersThesis{ yildiz2016,
    abstract  = {One of the major goals of research in Robotics is to
    develop truly autonomous vehicles that can be utilized to
    assist humans in dangerous environments, especially where
    human life is under threat. In this regard, Unmanned
    Surface Vehicles (USVs) are developed and utilized to cope
    up with military scenarios which put human life at
    potential risk. One of the very common scenarios is harbor
    security, where USVs are expected to conduct surveillance
    and feedback to strengthen the harbor's security. The (USV)
    Roboship has been designed and developed for harbor
    patrolling and coastal surveillance. It goes without
    saying, in such an application domain dependability plays a
    major role. USVs must not endanger the mission, face
    unexpected situations and allow no human intervention in
    case of a problem. At this point, it should be acknowledged
    that dependability is strongly coupled with fault tolerance
    when it comes to autonomous robots. However, due to
    complicated design schemes of USVs and unpredictable
    environmental factors of harbor environments, USVs are
    prone to fail. As it is almost certain that the USVs will
    fail, in order to not to endanger the mission, USVs should
    fail in a way that the recovery from the failures is
    possible, and mission integrity is protected. This paper
    highlights a fault tolerant software architecture for the
    USV Roboship. We conduct a comprehensive reliability
    analysis and propose a fault tolerant scheme, wherein the
    sensitive points of the existing architecture are spotted,
    and the fault tolerant techniques are incorporated. Since
    the approach is model-free and its decisions are made at a
    high frequency, the system is able to deal with highly
    dynamic scenarios. We used the UWSim simulation environment
    to simulate the scenarios in which the USV was supposed to
    navigate safely using its sensors. Field tests have proven
    the performance and reliability of the system to be
    satisfactory, yielding 53.57% decrease in faults. We plan
    to deploy the USV in Eckernfoerde harbor region of Germany
    to test the scheme in the future.},
    address  = {Grantham-Allee 20, 53757 St. Augustin, Germany},
    annote  = {SS13, Fraunhofer Institute IAIS - Roboship, Pl{\"o}ger,
    Alda, Perv{\"o}lz supervising},
    author  = {Yildiz, Erenus and {G. Ploeger}, Paul and Alda, Sascha and
    Pervoelz, Kai},
    month = {October 2016},
    school  = {Bonn-Rhein-Sieg University of Applied Sciences},
    title = {{Fault-Tolerant Software Architecture for the Unmanned
    Surface Vehicle Roboship Declaration of Authorship}},
    year = {2016}
    }

  • D. Vázquez, “Video Analysis and Anomaly Detection in Human Gait Patterns from a Fast Moving Camera – Development of an Outdoor Gait Analysis System,” Master Thesis, Grantham-Allee 20, 53757 St. Augustin, Germany, 2016.
    [BibTeX] [Abstract]

    Today, running has become an important activity in the life of many people as it mainly has a good impact on their health. Unfortunately, some individuals do not exercise in an appropriate way provoking several injuries to their bodies. Indoor gait analysis has been used to detect and treat abnormal patterns but it has been proven that the performance exhibited in indoor laboratories is not the same as the performance exhibited outdoors. If the gait patterns of the runners could be analyzed in environments in which they perform their outdoor running activities, better diagnostics would be obtained. Therefore, this Master thesis proposes a method where a moving robot could be used to record the runner’s performance by means of a depth camera. The proposed approach relies on a stereo vision system and color markers. Given a frontal stereo view of a subject, the location of the markers can be determined and used to create a 3D model of the human skeleton. Based on this model, several joint angles, and other features are created. Then, a Support Vector Machine is trained to differentiate between normal and abnormal gait patterns. The proposed method showed a classification rate of 76\% proving that frontal gait analysis is feasible.

    @MastersThesis{ 2016vazquezurenad,
    abstract  = {Today, running has become an important activity in the
    life of many people as it mainly has a good impact on their
    health. Unfortunately, some individuals do not exercise in
    an appropriate way provoking several injuries to their
    bodies. Indoor gait analysis has been used to detect and
    treat abnormal patterns but it has been proven that the
    performance exhibited in indoor laboratories is not the
    same as the performance exhibited outdoors. If the gait
    patterns of the runners could be analyzed in environments
    in which they perform their outdoor running activities,
    better diagnostics would be obtained. Therefore, this
    Master thesis proposes a method where a moving robot could
    be used to record the runner's performance by means of a
    depth camera. The proposed approach relies on a stereo
    vision system and color markers. Given a frontal stereo
    view of a subject, the location of the markers can be
    determined and used to create a 3D model of the human
    skeleton. Based on this model, several joint angles, and
    other features are created. Then, a Support Vector Machine
    is trained to differentiate between normal and abnormal
    gait patterns. The proposed method showed a classification
    rate of 76\% proving that frontal gait analysis is
    feasible.},
    address  = {Grantham-Allee 20, 53757 St. Augustin, Germany},
    annote  = {WS2014 - HBRS - Prof. Prassler, Prof. P\"oger, F\"uller},
    author  = {Daniel V\'azquez},
    month = {November},
    school  = {Bonn-Rhein-Sieg University of Applied Sciences},
    title = {Video Analysis and Anomaly Detection in Human Gait
    Patterns from a Fast Moving Camera - Development of an
    Outdoor Gait Analysis System},
    year = {2016}
    }

  • S. Sathanam, “Emotion detection from German text by sentiment analysis,” Master Thesis, Grantham-Allee 20, 53757 St. Augustin, Germany, 2016.
    [BibTeX] [Abstract]

    Social assistive robots(SAR) providing emotional care and support is investigated in Emorobot project to help the patients affected by dementia. One of the functions of the SAR is to detect emotion from the patients to communicate effectively. Detecting emotions directly via facial expressions or vocal features have certain demerits with the elderly people. Detection of emotions from the spoken text is considered in this work. The six basic emotions Happy, Sad, Anger, Fear, Surprise and disgust are considered in addition to emotionless(none) condition. Emotion detection which comes under sentiment analysis has major issues with the complexity of feature vectors representation and preserving semantic meanings. The emergence of word embeddings is a recent breakthrough in natural language processing, where huge amount of data are used to learn semantically preserved word and sentence vectors in an unsupervised manner. Application of word vectors and document vectors in emotion detection is studied in this work. Detecting emotions from text is handled in two phases. The first phase involves generation of feature vectors from vector models. Vector models require huge amount of dataset to generate high quality feature vectors. Dataset containing nearly 10 million sentences are created by streaming online tweets. Another dataset around 18 million wikipedia sentences are also used for comparison of the nature and quality of the vectors generated. Second phase is estimating emotions from the sentences with the use of feature vectors. A sentence could be represented both by document vectors as well as average of word vectors in the sentence. Training dataset is manually generated by applying automatic labeling on twitter dataset to label the tweets for emotions based on the hashtags attached. Evaluation of the model is performed on the annotated sentences from Emorobot project as ground truth. Due to lesser data, a test dataset from twitter is also evaluated. A detailed evaluation of the vector models is provided for the use case considered. In total, two datasets Twitter and Wiki are applied on two word vector models CBOW, SG and two document vector models DBOW and DM to generate feature vectors which are evaluated against two test datasets Twitter and annotated dataset. A result of 16 cases shows that generally averaged word vectors perform better than document vectors. In word vector models, CBOW model outperforms SG model but by only a small value. Document vectors are unpredictable in nature as the results vary drastically for each runs. Eventhough, the vectors generated from wikipedia datasets contains better features as evaluated by analogy test sentences. The vectors generated from twitter datasets achieves better results than the feature vectors learnt from wikipedia datasets.

    @MastersThesis{ 2016sathanam,
    abstract  = {Social assistive robots(SAR) providing emotional care and
    support is investigated in Emorobot project to help the
    patients affected by dementia. One of the functions of the
    SAR is to detect emotion from the patients to communicate
    effectively. Detecting emotions directly via facial
    expressions or vocal features have certain demerits with
    the elderly people. Detection of emotions from the spoken
    text is considered in this work. The six basic emotions
    Happy, Sad, Anger, Fear, Surprise and disgust are
    considered in addition to emotionless(none) condition.
    Emotion detection which comes under sentiment analysis has
    major issues with the complexity of feature vectors
    representation and preserving semantic meanings. The
    emergence of word embeddings is a recent breakthrough in
    natural language processing, where huge amount of data are
    used to learn semantically preserved word and sentence
    vectors in an unsupervised manner. Application of word
    vectors and document vectors in emotion detection is
    studied in this work. Detecting emotions from text is
    handled in two phases. The first phase involves generation
    of feature vectors from vector models. Vector models
    require huge amount of dataset to generate high quality
    feature vectors. Dataset containing nearly 10 million
    sentences are created by streaming online tweets. Another
    dataset around 18 million wikipedia sentences are also used
    for comparison of the nature and quality of the vectors
    generated. Second phase is estimating emotions from the
    sentences with the use of feature vectors. A sentence could
    be represented both by document vectors as well as average
    of word vectors in the sentence. Training dataset is
    manually generated by applying automatic labeling on
    twitter dataset to label the tweets for emotions based on
    the hashtags attached. Evaluation of the model is performed
    on the annotated sentences from Emorobot project as ground
    truth. Due to lesser data, a test dataset from twitter is
    also evaluated. A detailed evaluation of the vector models
    is provided for the use case considered. In total, two
    datasets Twitter and Wiki are applied on two word vector
    models CBOW, SG and two document vector models DBOW and DM
    to generate feature vectors which are evaluated against two
    test datasets Twitter and annotated dataset. A result of 16
    cases shows that generally averaged word vectors perform
    better than document vectors. In word vector models, CBOW
    model outperforms SG model but by only a small value.
    Document vectors are unpredictable in nature as the results
    vary drastically for each runs. Eventhough, the vectors
    generated from wikipedia datasets contains better features
    as evaluated by analogy test sentences. The vectors
    generated from twitter datasets achieves better results
    than the feature vectors learnt from wikipedia datasets.},
    address  = {Grantham-Allee 20, 53757 St. Augustin, Germany},
    annote  = {WS2013 - Emotion detection from German text by sentiment
    analysis, Pl{\"o}ger, Kraetzschmar, F{\"u}ller supervising},
    author  = {Sivasurya Sathanam},
    month = {April},
    school  = {Bonn-Rhein-Sieg University of Applied Sciences},
    title = {Emotion detection from German text by sentiment analysis},
    year = {2016}
    }

  • D. E. Ramos Avila, “A Study on Swarm Intelligence: Towards Nature-Inspired Robot Navigation,” Master Thesis, Grantham-Allee 20, 53757 St. Augustin, Germany, 2016.
    [BibTeX] [Abstract]

    Swarm Intelligence (SI) is a problem-solving approach whose inspiration comes from cooperative behaviours and well-defined social structures observed in different animal species. Within those structures, simple interactions among “unsophisticated” individuals produce complex and interesting properties which aid flocks of birds, schools of fish, packs of predators and colonies of insects in solving daily tasks in a very efficient way. Several meta-heuristics such as the particle swarm optimization (PSO) and the artificial bee colony algorithm (ABC) are based on this metaphor and entail important benefits. One of their most remarkable advantages is their ability to find solutions to NP-hard problems in a very short time. In the field of robotics, motion planning is a well-known problem considered to be NP-hard. This work studies SI as a possible alternative to state-of-the-art global path planners, with the main objective of producing robot-traversable paths in a competitive amount of time without further optimization. In particular, the usage of the novel grey wolf optimizer (GWO) is proposed and tested against other SI-based meta-heuristics, and then again against popular sampling-based planners. The experimental evaluation indicates that GWO usually has a higher success rate and faster convergence than other SI-based algorithms like ABC and the firefly algorithm (FFA). Moreover, the results show that even though rapidly-exploring random trees (RRT) are much faster, the proposed strategy produces shorter and smoother paths and even often outperforms probabilistic roadmaps (PRM) in terms of computational time. In spite of the limitations that the greedy nature of the planner still poses when dealing with complex environments, the results highlight a promising line of research that might alleviate current issues in motion planning. The approach is further extended to address multiple objective functions using the multi-objective grey wolf optimizer (MOGWO) based on Pareto dominance, as a typical requirement of robot motion planning is not to produce the shortest possible path, but rather a short-enough, smooth-enough and safe-enough path.

    @MastersThesis{ 2016ramosavila,
    title = {A Study on Swarm Intelligence: Towards Nature-Inspired
    Robot Navigation},
    author  = {Ramos Avila, Diego Enrique},
    school  = {Bonn-Rhein-Sieg University of Applied Sciences},
    year = {2016},
    address  = {Grantham-Allee 20, 53757 St. Augustin, Germany},
    month = {April},
    abstract  = {Swarm Intelligence (SI) is a problem-solving approach
    whose inspiration comes from cooperative behaviours and
    well-defined social structures observed in different animal
    species. Within those structures, simple interactions among
    “unsophisticated” individuals produce complex and
    interesting properties which aid flocks of birds, schools
    of fish, packs of predators and colonies of insects in
    solving daily tasks in a very efficient way. Several
    meta-heuristics such as the particle swarm optimization
    (PSO) and the artificial bee colony algorithm (ABC) are
    based on this metaphor and entail important benefits. One
    of their most remarkable advantages is their ability to
    find solutions to NP-hard problems in a very short time. In
    the field of robotics, motion planning is a well-known
    problem considered to be NP-hard. This work studies SI as a
    possible alternative to state-of-the-art global path
    planners, with the main objective of producing
    robot-traversable paths in a competitive amount of time
    without further optimization. In particular, the usage of
    the novel grey wolf optimizer (GWO) is proposed and tested
    against other SI-based meta-heuristics, and then again
    against popular sampling-based planners. The experimental
    evaluation indicates that GWO usually has a higher success
    rate and faster convergence than other SI-based algorithms
    like ABC and the firefly algorithm (FFA). Moreover, the
    results show that even though rapidly-exploring random
    trees (RRT) are much faster, the proposed strategy produces
    shorter and smoother paths and even often outperforms
    probabilistic roadmaps (PRM) in terms of computational
    time. In spite of the limitations that the greedy nature of
    the planner still poses when dealing with complex
    environments, the results highlight a promising line of
    research that might alleviate current issues in motion
    planning. The approach is further extended to address
    multiple objective functions using the multi-objective grey
    wolf optimizer (MOGWO) based on Pareto dominance, as a
    typical requirement of robot motion planning is not to
    produce the shortest possible path, but rather a
    short-enough, smooth-enough and safe-enough path.},
    annote  = {WS13/14 H-BRS - A Study on Swarm Intelligence: Towards
    Nature-Inspired Robot Navigation Pl{\"o}ger, Asteroth
    supervising}
    }

  • A. M. Sundaram, “Planning Realistic Force Interactions for Bimanual Grasping and Manipulation,” Master Thesis, Grantham-Allee 20, 53757 St. Augustin, Germany, 2016.
    [BibTeX] [Abstract]

    Dexterous robot hands offer a wide variety of grasping and interaction possibilities with objects. In order to select the best grasp, it is critical to count with a reliable grasp quality measure. Traditional grasp analysis methods use quality measures that allow a relative comparison of grasps for the same object, without an associated physical meaning for the resulting quality. The focus of this thesis is to establish an improved grasp analysis method that will result in a quality measure that can be directly interpreted in the force domain. One of the most commonly used grasp qualities is the largest minimum resisted wrench, which indicates the maximum perturbation wrench that a grasp can resist in any direction. Two efficient ways to calculate this quality are identified: (i) incremental grasp wrench space algorithm, and (ii) ray shooting algorithm. However, existing algorithms for such methods make several assumptions to avoid computational complexities in analyzing the 6D wrench space of a grasp. Important properties like hand actuation, realizable contact forces, friction at the contacts, and geometry of the object to be grasped are either neglected or greatly simplified. In this thesis, these assumptions are improved to bring those algorithms closer to reality. In the case of bimanual grasps, the number of contacts significantly increases, which in turn increases the computational complexity of the process. Suitable algorithms to handle a higher number of contacts are also proposed in this thesis. For grasping an object successfully, considering the hand and the object for analysis are necessary but not sufficient requirements. The capabilities of the robotic arm to which the hand is attached are equally important. Different manipulability measures are considered for the arm, corresponding to single and dual hand grasps, and they are later unified with the physically relevant grasp quality to obtain an overall measure of the goodness of a particular grasp. Based on the updated grasp quality, a complete grasp planning architecture is established. It also includes methods for bimanual grasp synthesis and grasp filtering based on properties like collision with the environment and arm reachability. The thesis includes application examples that illustrate the applicability of the approach. Finally, the developed algorithms can be generalized to a different type of force interaction task, namely a humanoid robot balancing with multiple contacts with the environment. A customized ray shooting algorithm is used to find the stability region of a humanoid legged robot standing on an uneven terrain or making multiple contacts with its hands and legs. In contrast to the regular zero-moment point based method, the stability region is found by analyzing the wrench space of the robot, which makes the method independent of the number of contacts or the contact configuration. Different examples show the direct and intuitive interpretation of the results obtained with the proposed method.

    @MastersThesis{ 2016meenakshisundaram,
    abstract  = {Dexterous robot hands offer a wide variety of grasping and
    interaction possibilities with objects. In order to select
    the best grasp, it is critical to count with a reliable
    grasp quality measure. Traditional grasp analysis methods
    use quality measures that allow a relative comparison of
    grasps for the same object, without an associated physical
    meaning for the resulting quality. The focus of this thesis
    is to establish an improved grasp analysis method that will
    result in a quality measure that can be directly
    interpreted in the force domain.
    One of the most commonly used grasp qualities is the
    largest minimum resisted wrench, which indicates the
    maximum perturbation wrench that a grasp can resist in any
    direction. Two efficient ways to calculate this quality are
    identified: (i) incremental grasp wrench space algorithm,
    and (ii) ray shooting algorithm. However, existing
    algorithms for such methods make several assumptions to
    avoid computational complexities in analyzing the 6D wrench
    space of a grasp. Important properties like hand actuation,
    realizable contact forces, friction at the contacts, and
    geometry of the object to be grasped are either neglected
    or greatly simplified. In this thesis, these assumptions
    are improved to bring those algorithms closer to reality.
    In the case of bimanual grasps, the number of contacts
    significantly increases, which in turn increases the
    computational complexity of the process. Suitable
    algorithms to handle a higher number of contacts are also
    proposed in this thesis.
    For grasping an object successfully, considering the hand
    and the object for analysis are necessary but not
    sufficient requirements. The capabilities of the robotic
    arm to which the hand is attached are equally important.
    Different manipulability measures are considered for the
    arm, corresponding to single and dual hand grasps, and they
    are later unified with the physically relevant grasp
    quality to obtain an overall measure of the goodness of a
    particular grasp. Based on the updated grasp quality, a
    complete grasp planning architecture is established. It
    also includes methods for bimanual grasp synthesis and
    grasp filtering based on properties like collision with the
    environment and arm reachability. The thesis includes
    application examples that illustrate the applicability of
    the approach.
    Finally, the developed algorithms can be generalized to a
    different type of force interaction task, namely a humanoid
    robot balancing with multiple contacts with the
    environment. A customized ray shooting algorithm is used to
    find the stability region of a humanoid legged robot
    standing on an uneven terrain or making multiple contacts
    with its hands and legs. In contrast to the regular
    zero-moment point based method, the stability region is
    found by analyzing the wrench space of the robot, which
    makes the method independent of the number of contacts or
    the contact configuration. Different examples show the
    direct and intuitive interpretation of the results obtained
    with the proposed method.},
    address  = {Grantham-Allee 20, 53757 St. Augustin, Germany},
    annote  = {SS13 DLR - Planning Realistic Force Interactions for
    Bimanual Grasping and Manipulation Kraetzschmar,
    Albu-Schaeffer, Roa, Schneider supervising},
    author  = {Ashok Meenakshi Sundaram},
    month = {June},
    school  = {Bonn-Rhein-Sieg University of Applied Sciences},
    title = {Planning Realistic Force Interactions for Bimanual
    Grasping and Manipulation},
    year = {2016}
    }

  • O. L. Carrion, “Task Planning, Execution and Monitoring for Mobile Manipulators in Industrial Domains,” Master Thesis, Grantham-Allee 20, 53757 St. Augustin, Germany, 2016.
    [BibTeX] [Abstract]

    Decision making is a necessary skill for autonomous mobile manipulators working in industrial environments. Task planning is a well-established field with a large research community that continues to produce new heuristics, tools and algorithms that enable agents to produce plans that achieve their goals. In this work we demonstrate the applicability of satisficing task planning with action costs by integrating a state-of-the-art planner, the Mercury planner, into the KUKA youBot mobile manipulator and using it to solve basic transportation and insertion tasks. In contrast to optimal planners which minimize total action costs in a plan, satisficing planners minimize plan generation time, yielding sub-optimal solutions but in less time compared to optimal planners. The planner uses a delete-list relaxation heuristic to quickly prune the search space and generate satisfactory solutions. The developed planning framework is modular, re-usable and well documented. It brings together two major standards in robotics and task planning: ROS and PDDL, similar to ROSPlan. Unlike ROSPlan, this work is able to plan with cost information. Moreover, it is more modular, enabling the community to use the various components at their own discretion. Finally, while ROSPlan allows only one re-planning strategy, our framework enables users to quickly implement their own strategies. The resulting system demonstrates that the agent’s behavior is optimized, it is able to flexibly handle unexpected situations, and it can robustly handle failures by re-planning when needed. It is also easier to maintain and extend. The work presented here also highlights the benefits of conducting a domain analysis to gain the maximum benefit from the use of a given planner and domain.

    @MastersThesis{ 2016limaoscar,
    abstract  = {Decision making is a necessary skill for autonomous mobile
    manipulators working in industrial environments. Task
    planning is a well-established field with a large research
    community that continues to produce new heuristics, tools
    and algorithms that enable agents to produce plans that
    achieve their goals. In this work we demonstrate the
    applicability of satisficing task planning with action
    costs by integrating a state-of-the-art planner, the
    Mercury planner, into the KUKA youBot mobile manipulator
    and using it to solve basic transportation and insertion
    tasks. In contrast to optimal planners which minimize total
    action costs in a plan, satisficing planners minimize plan
    generation time, yielding sub-optimal solutions but in less
    time compared to optimal planners. The planner uses a
    delete-list relaxation heuristic to quickly prune the
    search space and generate satisfactory solutions. The
    developed planning framework is modular, re-usable and well
    documented. It brings together two major standards in
    robotics and task planning: ROS and PDDL, similar to
    ROSPlan. Unlike ROSPlan, this work is able to plan with
    cost information. Moreover, it is more modular, enabling
    the community to use the various components at their own
    discretion. Finally, while ROSPlan allows only one
    re-planning strategy, our framework enables users to
    quickly implement their own strategies. The resulting
    system demonstrates that the agent's behavior is optimized,
    it is able to flexibly handle unexpected situations, and it
    can robustly handle failures by re-planning when needed. It
    is also easier to maintain and extend. The work presented
    here also highlights the benefits of conducting a domain
    analysis to gain the maximum benefit from the use of a
    given planner and domain.},
    address  = {Grantham-Allee 20, 53757 St. Augustin, Germany},
    annote  = {SS04/13 FH-BRS - RoboCup@Work Pl{\"o}ger, Kraetzschmar,
    Awaad supervising},
    author  = {Oscar Lima Carrion},
    month = {April},
    school  = {Bonn-Rhein-Sieg University of Applied Sciences},
    title = {Task Planning, Execution and Monitoring for Mobile
    Manipulators in Industrial Domains},
    year = {2016}
    }

  • A. Mitrevski, “Improving the Manipulation Skills of Service Robots by Refining Action Representations,” Master Thesis, Grantham-Allee 20, 53757 Sankt Augustin, Germany, 2016.
    [BibTeX] [Abstract]

    Releasing objects is an error-prone robot manipulation activity often due to insufficient knowledge about the preconditions of releasing actions. As the problem has a semantic nature that stems from the variations in physical behaviour between different object categories, neither symbolic nor geometric models alone are enough for specifying how and where a releasing action should be executed. In principle, those two have to be combined into a more general representation that maximises the execution success and minimises the probability of failures; however, symbolic and geometric action representations are frequently studied in isolation and one of them is taken for granted. This thesis investigates the exact nature of releasing actions, the problem of learning reusable models of such actions, and the manner in which those models can be utilised for execution. Starting with an examination of multiple planning domain learning algorithms and geometric reasoning procedures, we develop a template-based representation of actions that simplifies the acquisition and application of symbolic and geometric models; in particular, we show that template models, which we call $\delta$ and $\delta^\varnothing$ problems, can be learned in special configurations and that pairwise object relations should be an inherent part of the models’ nature. The models are organised in an object-centred memory model, called $\Delta$ memory, in which storage and retrieval depend on semantic object information. With the help of a simulated environment that abstracts away various manipulation activities, we show that properly constructed $\delta$ and $\delta^\varnothing$ problems can be a reliable representation of releasing actions, thereby justifying the use of template-based action representations, but also demonstrate that their utility depends on a number of conventions and can benefit from an additional ontology.

    @MastersThesis{ 2016mitrevski,
    title = {Improving the Manipulation Skills of Service Robots by
    Refining Action Representations},
    author  = {Aleksandar Mitrevski},
    abstract  = {Releasing objects is an error-prone robot manipulation
    activity often due to insufficient knowledge about the
    preconditions of releasing actions. As the problem has a
    semantic nature that stems from the variations in physical
    behaviour between different object categories, neither
    symbolic nor geometric models alone are enough for
    specifying how and where a releasing action should be
    executed. In principle, those two have to be combined into
    a more general representation that maximises the execution
    success and minimises the probability of failures; however,
    symbolic and geometric action representations are
    frequently studied in isolation and one of them is taken
    for granted.
    This thesis investigates the exact nature of releasing
    actions, the problem of learning reusable models of such
    actions, and the manner in which those models can be
    utilised for execution. Starting with an examination of
    multiple planning domain learning algorithms and geometric
    reasoning procedures, we develop a template-based
    representation of actions that simplifies the acquisition
    and application of symbolic and geometric models; in
    particular, we show that template models, which we call
    $\delta$ and $\delta^\varnothing$ problems, can be learned
    in special configurations and that pairwise object
    relations should be an inherent part of the models' nature.
    The models are organised in an object-centred memory model,
    called $\Delta$ memory, in which storage and retrieval
    depend on semantic object information.
    With the help of a simulated environment that abstracts
    away various manipulation activities, we show that properly
    constructed $\delta$ and $\delta^\varnothing$ problems can
    be a reliable representation of releasing actions, thereby
    justifying the use of template-based action
    representations, but also demonstrate that their utility
    depends on a number of conventions and can benefit from an
    additional ontology.},
    school  = {Bonn-Rhein-Sieg University of Applied Sciences},
    address  = {Grantham-Allee 20, 53757 Sankt Augustin, Germany},
    month = {January},
    year = {2016},
    annote  = {WS13/14 HBRS - Pl{\"o}ger, Witt, Kuestenmacher
    supervising}
    }

  • D. Nair, “Predicting Object Locations using Spatio-Temporal Information by a Domestic Service Robot: A Bayesian Learning Approach,” Master Thesis, Grantham-Allee 20, 53757 St. Augustin, Germany, 2016.
    [BibTeX] [Abstract]

    One of the ways domestic service robots can better assist humans is by providing personalized, predictive and context-aware services. Robots can observe human activities and work patterns and provide time-based contextual assistance. This thesis aims to enable domestic robots to empirically learn about human behaviour and preferences. In the current literature, user preferences are learned for a generic home environment, on the contrary we learn preferences over a specific home. Robots generate a lot of information using the raw data from their sensors, which is often discarded after use. If this information is recorded, it can be used to generate new knowledge. The thesis proposes models to generate knowledge about user preferences using stored information. The developed approaches in this thesis cover the following two knowledge generation topics: (1) learning user location preferences (2) learning user preferences in object placement. All knowledge generation techniques developed in this thesis are based on Bayesian modelling and have been implemented using probabilistic programming languages. The learned user preferences were used by the robot for predicting: (a) location of non-stationary objects (b) location of users in home and (c) room occupancy. The models were evaluated on three datasets collected over several months containing person and object occurrences in home and office environments. The efficiency of the models was accessed by measuring the accuracy score of each models. Our model for predicting location of non-stationary objects (a) was able to predict with 70% accuracy for 26 objects, while The model for user location preference (b) was able to predict with 63% accuracy and model for room occupancy (c) could predict for 3 rooms with more than 80% accuracy.

    @MastersThesis{ 2016nairdeebul,
    abstract  = {One of the ways domestic service robots can better assist
    humans is by providing personalized, predictive and
    context-aware services. Robots can observe human activities
    and work patterns and provide time-based contextual
    assistance. This thesis aims to enable domestic robots to
    empirically learn about human behaviour and preferences. In
    the current literature, user preferences are learned for a
    generic home environment, on the contrary we learn
    preferences over a specific home. Robots generate a lot of
    information using the raw data from their sensors, which is
    often discarded after use. If this information is recorded,
    it can be used to generate new knowledge. The thesis
    proposes models to generate knowledge about user
    preferences using stored information. The developed
    approaches in this thesis cover the following two knowledge
    generation topics: (1) learning user location preferences
    (2) learning user preferences in object placement. All
    knowledge generation techniques developed in this thesis
    are based on Bayesian modelling and have been implemented
    using probabilistic programming languages. The learned user
    preferences were used by the robot for predicting: (a)
    location of non-stationary objects (b) location of users in
    home and (c) room occupancy. The models were evaluated on
    three datasets collected over several months containing
    person and object occurrences in home and office
    environments. The efficiency of the models was accessed by
    measuring the accuracy score of each models. Our model for
    predicting location of non-stationary objects (a) was able
    to predict with 70% accuracy for 26 objects, while The
    model for user location preference (b) was able to predict
    with 63% accuracy and model for room occupancy (c) could
    predict for 3 rooms with more than 80% accuracy.},
    address  = {Grantham-Allee 20, 53757 St. Augustin, Germany},
    annote  = {SS14 Pl{\"o}ger, Lakemeyer, Niemueller supervising},
    author  = {Deebul Nair},
    month = {September},
    school  = {Bonn-Rhein-Sieg University of Applied Sciences},
    title = {Predicting Object Locations using Spatio-Temporal
    Information by a Domestic Service Robot: A Bayesian
    Learning Approach},
    year = {2016}
    }

  • A. Ortega Sáinz, “Multi-Robot Path Planning in Confined Dynamic Environments,” Master Thesis, Grantham-Allee 20, 53757 St. Augustin, Germany, 2016.
    [BibTeX] [Abstract]

    The automation of transportation tasks in logistics using autonomous robots has been gaining popularity in the last years, due in most part to the advantages they present against other automation technologies or manually operated solutions. The productivity and efficiency of an application employing a Multi-Robot System (MRS) is a direct result of the path planning and coordination strategy used. The Multi-Robot Path Planning (MRPP) problem has been widely researched throughout the years, and given the popularity of the topic and the amount of algorithms available, selecting the best approach for a given application is not as straightforward as it may seem. First of all, there is still a gap between the problems solved by the academic community in the state of the art and the capabilities of modern technologies used in real applications. Numerous efforts are being made to close this gap, but given the complexities of evaluating multi-robot systems with real robots, there is still a very long way to go. Furthermore, the lack of a standard methodology to evaluate MRPP problems makes the selection of an approach for a given application difficult, particularly since the reported results are not directly comparable. Using a hospital transportation task as an example use case, this thesis will benchmark decentralized path planning approaches, keeping in mind real-life conditions, oriented towards implementation rather than theory. A comparative analysis will asses the selected approaches in regards to their scalability in the number of robots and their robustness to dynamic environments populated by moving obstacles. An analysis of the evaluation methodology and performance metrics reported in the state of the art will be use as a basis for the proposed set of guidelines and performance metrics to benchmark MRPP approaches hereafter. Finally, the groundwork for a benchmarking framework will be presented.

    @MastersThesis{ 2016ortega,
    abstract  = {The automation of transportation tasks in logistics using
    autonomous robots has been gaining popularity in the last
    years, due in most part to the advantages they present
    against other automation technologies or manually operated
    solutions. The productivity and efficiency of an
    application employing a Multi-Robot System (MRS) is a
    direct result of the path planning and coordination
    strategy used. The Multi-Robot Path Planning (MRPP) problem
    has been widely researched throughout the years, and given
    the popularity of the topic and the amount of algorithms
    available, selecting the best approach for a given
    application is not as straightforward as it may seem. First
    of all, there is still a gap between the problems solved by
    the academic community in the state of the art and the
    capabilities of modern technologies used in real
    applications. Numerous efforts are being made to close this
    gap, but given the complexities of evaluating multi-robot
    systems with real robots, there is still a very long way to
    go. Furthermore, the lack of a standard methodology to
    evaluate MRPP problems makes the selection of an approach
    for a given application difficult, particularly since the
    reported results are not directly comparable. Using a
    hospital transportation task as an example use case, this
    thesis will benchmark decentralized path planning
    approaches, keeping in mind real-life conditions, oriented
    towards implementation rather than theory. A comparative
    analysis will asses the selected approaches in regards to
    their scalability in the number of robots and their
    robustness to dynamic environments populated by moving
    obstacles. An analysis of the evaluation methodology and
    performance metrics reported in the state of the art will
    be use as a basis for the proposed set of guidelines and
    performance metrics to benchmark MRPP approaches hereafter.
    Finally, the groundwork for a benchmarking framework will
    be presented.},
    address  = {Grantham-Allee 20, 53757 St. Augustin, Germany},
    annote  = {WS13/14 HBRS Prassler, Pl{\"o}ger, F{\"u}ller supervising},
    author  = {Argentina {Ortega S{\'a}inz}},
    keywords  = {multi-robot systems; multi-robot path planning},
    month = {July},
    school  = {Bonn-Rhein-Sieg University of Applied Sciences},
    title = {Multi-Robot Path Planning in Confined Dynamic
    Environments},
    year = {2016}
    }

  • N. Deshpande, “Using Semantic Information for Robot Navigation,” Master Thesis, Grantham-Allee 20, 53757 St. Augustin, Germany, 2016.
    [BibTeX] [Abstract]

    Current research in robotics focuses on making autonomous robots for domestic purposes which have to work along with humans in real environments. One of the prerequisites for such robots is the ability to navigate intelligently in their environment. Traditional navigation systems focus on finding a shortest collision free path by avoiding all obstacles. This approach has worked well in laboratories and structured environments. However, for robots navigating in real environments that are filled with humans, often, finding a shortest collision free path is not the desired behaviour. The robot needs to navigate in a more intelligent manner by adapting its behaviour based on the context at hand. Moreover, real environments are unstructured and there are often situations where a robot needs to reposition objects to navigate and reach its goal. This work addresses the problem of using semantic information for making navigation adaptive to different situations. Towards this goal, different forms of semantic information useful for navigation have been identified. An architecture is proposed to represent this information and use it for different aspects of navigation. The proposed architecture also uses contextual information about the tasks at hand for navigation. The architecture has been developed and implemented using the Robot Operating System(ROS) framework. Tests have been performed in simulation using the Pioneer 3DX robot platform. Preliminary results prove the validity of the proposed architecture. The robot was able to navigate in a more desired and intelligent manner by using semantic and contextual information.

    @MastersThesis{ 2016deshpande,
    title = {Using Semantic Information for Robot Navigation},
    author  = {Niranjan Deshpande},
    school  = {Bonn-Rhein-Sieg University of Applied Sciences},
    year = {2016},
    address  = {Grantham-Allee 20, 53757 St. Augustin, Germany},
    month = {March},
    note = {Advisors: Prof. Dr. Paul G. Pl{\"o}ger, Prof. Dr. Anne
    Spalanzani, Mr. Sven Schneider},
    abstract  = {Current research in robotics focuses on making autonomous
    robots for domestic purposes which have to work along with
    humans in real environments. One of the prerequisites for
    such robots is the ability to navigate intelligently in
    their environment. Traditional navigation systems focus on
    finding a shortest collision free path by avoiding all
    obstacles. This approach has worked well in laboratories
    and structured environments. However, for robots navigating
    in real environments that are filled with humans, often,
    finding a shortest collision free path is not the desired
    behaviour. The robot needs to navigate in a more
    intelligent manner by adapting its behaviour based on the
    context at hand. Moreover, real environments are
    unstructured and there are often situations where a robot
    needs to reposition objects to navigate and reach its goal.
    This work addresses the problem of using semantic
    information for making navigation adaptive to different
    situations. Towards this goal, different forms of semantic
    information useful for navigation have been identified. An
    architecture is proposed to represent this information and
    use it for different aspects of navigation. The proposed
    architecture also uses contextual information about the
    tasks at hand for navigation. The architecture has been
    developed and implemented using the Robot Operating
    System(ROS) framework. Tests have been performed in
    simulation using the Pioneer 3DX robot platform.
    Preliminary results prove the validity of the proposed
    architecture. The robot was able to navigate in a more
    desired and intelligent manner by using semantic and
    contextual information.}
    }

2015

  • M. Zolghadr, “Semantic Similarity Between Objects in Home Environments,” Master Thesis, Grantham-Allee 20, 53757 St. Augustin, Germany, 2015.
    [BibTeX] [Abstract]

    When a service robot is acting in a home environment, there are situations where some of the objects necessary for completing the task may not be available, because either they are missing or the preconditions for using them are not met. In such situations, humans show incredible flexibility by making substitutions. However, for a robot identifying the similarity between objects and finding an appropriate substitute for an unavailable object is not a straightforward task. Most of the approaches which tried to solve the problem have focused on lifting, where another instance of the same object class is used as a substitute. These approaches apply a limited range of semantics. Most approaches mea- sure similarity between objects using perception. Perception-based approaches tend to be time-consuming. However, the application of semantic-based similarity measures has been successfully and efficiently used in the genetics domain. The approach presented here combines similarity measurement with knowledge base queries in order to find appropriate substitutes. In our ontological approach, similarity measurement is carried out based on a broad range of objects semantics, including the functional affordances, object features, part-whole relations, and spatial proximity of the objects in the home environment. In this research, existing tools, previously used in the biological domains, for measuring the similarity of individuals within an ontology are evaluated and an analysis of the most useful measures is conducted. The experiments which were conducted were used to guide the creation of a methodology for the modeling of the objects within the knowledge base. The results show that the Jaccard Similarity Coefficient is particularly well-suited to our goal. The results also highlight the limitations of this approach and suggest that a com- bination of ontology-based and perception-based approaches may be optimal in order to find a suitable substitute for the unavailable objects.

    @MastersThesis{ 2015zolghadr,
    abstract  = { When a service robot is acting in a home environment,
    there are situations where some of the objects necessary
    for completing the task may not be available, because
    either they are missing or the preconditions for using them
    are not met. In such situations, humans show incredible
    flexibility by making substitutions. However, for a robot
    identifying the similarity between objects and finding an
    appropriate substitute for an unavailable object is not a
    straightforward task. Most of the approaches which tried to
    solve the problem have focused on lifting, where another
    instance of the same object class is used as a substitute.
    These approaches apply a limited range of semantics. Most
    approaches mea- sure similarity between objects using
    perception. Perception-based approaches tend to be
    time-consuming. However, the application of semantic-based
    similarity measures has been successfully and efficiently
    used in the genetics domain. The approach presented here
    combines similarity measurement with knowledge base queries
    in order to find appropriate substitutes. In our
    ontological approach, similarity measurement is carried out
    based on a broad range of objects semantics, including the
    functional affordances, object features, part-whole
    relations, and spatial proximity of the objects in the home
    environment. In this research, existing tools, previously
    used in the biological domains, for measuring the
    similarity of individuals within an ontology are evaluated
    and an analysis of the most useful measures is conducted.
    The experiments which were conducted were used to guide the
    creation of a methodology for the modeling of the objects
    within the knowledge base. The results show that the
    Jaccard Similarity Coefficient is particularly well-suited
    to our goal. The results also highlight the limitations of
    this approach and suggest that a com- bination of
    ontology-based and perception-based approaches may be
    optimal in order to find a suitable substitute for the
    unavailable objects. },
    address  = {Grantham-Allee 20, 53757 St. Augustin, Germany},
    annote  = {WS08/09 Kraetzschmar, Pl{\"o}ger, Awaad supervising},
    author  = {Mahan Zolghadr},
    month = {April},
    school  = {Bonn-Rhein-Sieg University of Applied Sciences},
    title = {Semantic Similarity Between Objects in Home Environments},
    year = {2015}
    }

  • M. Vayugundla, “Experimental Evaluation and Improvement of a Viewframe-Based Navigation Method,” Master Thesis, Grantham-Allee 20, 53757 St. Augustin, Germany, 2015.
    [BibTeX] [Abstract]

    Insects like ants and bees navigate robustly in their environments in spite of their small brains using vision as their primary sensor. Inspired by this, researchers at DLR are working on a range free navigation system using visual features. This ability is specially useful for autonomous navigation in large environments and also for computationally limited small robots. Each location in the environment is represented as a viewframe. A viewframe is a set of landmark observations where each landmark observation contains landmark I.D, descriptor and corresponding angle with respect to the robot’s location. Binary Robust Invariant Scalable Keypoints (BRISK) features extracted from the omnidirectional images were used as landmarks in this work. The environment is represented as a Trail-Map which preserves the relationship between adjacent viewframes and is efficient at both storing and pruning the map size when required. This work experimentally evaluates the current system and improves it. In this work, as an extension to the Trail-Map representation, topological knowledge was extracted with the help of dimensionality reduction techniques and by defining dissimilarity measures between any two viewframes. Using this topological knowledge, a pose graph is developed adding edges between viewframes based on how close they are in addition to the adjacency connections. With the help of this map, shorter paths were identified for homing. The topological mapping pipeline was implemented on the robot and experiments were performed in both indoor and outdoor environments. The performance of different dissimilarity measures and dimensionality reduction techniques in building a topological map of viewframes was evaluated. The experiments showed that using this pose graph representation, the robot could take shorter paths which are a subset of the long exploration paths by using the intersections of the paths.

    @MastersThesis{ 2015vayugundla,
    abstract  = {Insects like ants and bees navigate robustly in their
    environments in spite of their small brains using vision as
    their primary sensor. Inspired by this, researchers at DLR
    are working on a range free navigation system using visual
    features. This ability is specially useful for autonomous
    navigation in large environments and also for
    computationally limited small robots.
    Each location in the environment is represented as a
    viewframe. A viewframe is a set of landmark observations
    where each landmark observation contains landmark I.D,
    descriptor and corresponding angle with respect to the
    robot's location. Binary Robust Invariant Scalable
    Keypoints (BRISK) features extracted from the
    omnidirectional images were used as landmarks in this work.
    The environment is represented as a Trail-Map which
    preserves the relationship between adjacent viewframes and
    is efficient at both storing and pruning the map size when
    required. This work experimentally evaluates the current
    system and improves it.
    In this work, as an extension to the Trail-Map
    representation, topological knowledge was extracted with
    the help of dimensionality reduction techniques and by
    defining dissimilarity measures between any two viewframes.
    Using this topological knowledge, a pose graph is developed
    adding edges between viewframes based on how close they are
    in addition to the adjacency connections. With the help of
    this map, shorter paths were identified for homing. The
    topological mapping pipeline was implemented on the robot
    and experiments were performed in both indoor and outdoor
    environments. The performance of different dissimilarity
    measures and dimensionality reduction techniques in
    building a topological map of viewframes was evaluated.
    The experiments showed that using this pose graph
    representation, the robot could take shorter paths which
    are a subset of the long exploration paths by using the
    intersections of the paths.},
    address  = {Grantham-Allee 20, 53757 St. Augustin, Germany},
    annote  = {SS11 Heiden, Kraetzschmar, Stelzer supervising},
    author  = {Mallikarjuna Vayugundla},
    month = {December},
    school  = {Bonn-Rhein-Sieg University of Applied Sciences},
    title = {Experimental Evaluation and Improvement of a
    Viewframe-Based Navigation Method},
    year = {2015}
    }

  • J. Sanchez, “Robust and Safe Manipulation by Sensor Fusion of Robotic Manipulators and End-Effectors,” Master Thesis, Grantham-Allee 20, 53757 St. Augustin, Germany, 2015.
    [BibTeX] [Abstract]

    A continuously increasing demand of staff able to take care of the elderly has generated interest in using robots as caregivers. However, the current state of the art limits the complexity of the tasks a robot can achieve. Another important constraint, is the ability of the robot to safely react to unexpected behaviors of a person. Thus, the first step is ensuring the robot can detect these events. In this work, a multi-sensor system based on design patterns is proposed to detect an object (e.g. a person’s arm) slipping from the robot’s grasp. Tactile sensors are used in combination with a force-torque sensor to provide complementary information. Over 500 experiments on a Care-O-bot 3 provided a comparative evaluation of a slip detector implementation. The results show an improved performance when combining both modalities (tactile and force). Furthermore, the proposed implementation proved is able to operate with different grasp shapes while maintaining a high accuracy. Lastly, the evaluation also exhibited the difficulties encountered at detecting motions of a human arm being grasped by a robot.

    @MastersThesis{ 2015sanchez,
    abstract  = {A continuously increasing demand of staff able to take
    care of the elderly has generated interest in using robots
    as caregivers. However, the current state of the art limits
    the complexity of the tasks a robot can achieve. Another
    important constraint, is the ability of the robot to safely
    react to unexpected behaviors of a person. Thus, the first
    step is ensuring the robot can detect these events. In this
    work, a multi-sensor system based on design patterns is
    proposed to detect an object (e.g. a person's arm) slipping
    from the robot's grasp. Tactile sensors are used in
    combination with a force-torque sensor to provide
    complementary information. Over 500 experiments on a
    Care-O-bot 3 provided a comparative evaluation of a slip
    detector implementation. The results show an improved
    performance when combining both modalities (tactile and
    force). Furthermore, the proposed implementation proved is
    able to operate with different grasp shapes while
    maintaining a high accuracy. Lastly, the evaluation also
    exhibited the difficulties encountered at detecting motions
    of a human arm being grasped by a robot.},
    address  = {Grantham-Allee 20, 53757 St. Augustin, Germany},
    annote  = {WS12/13 BRSU - RoboCup Pl{\"o}ger, Kraetzschmar,
    Schneider, supervising},
    author  = {Jose Sanchez},
    month = {March},
    school  = {Bonn-Rhein-Sieg University of Applied Sciences},
    title = {Robust and Safe Manipulation by Sensor Fusion of Robotic
    Manipulators and End-Effectors},
    year = {2015}
    }

  • F. Kilic, “Application and improvement of the TRRL (Transport and Road Research Laboratory) high-speed laser profilometer algorithm with sensor fusion,” Master Thesis, Grantham-Allee 20, 53757 St. Augustin, Germany, 2015.
    [BibTeX] [Abstract]

    Maintenance of the highway networks is considered to be vital in order to ensure transportation safety and quality. Within different quality criterions used for a highway, road roughness stands out as the most important factor which deteriorates the driving comfort and endangers the passengers. High-speed profilometers are developed in order to be able to monitor road roughness on the highways without affecting the normal traffic flow. These devices record road elevation profiles while driving at highways speeds. Transportation and Road Research Laboratory (TRRL) has developed a unique high- speed profilometer system. Their design contains four laser displacement transducers which are placed along a long trailer to be towed by a vehicle. As the trailer moves forward, the leading sensor measures the road elevation and the other sensors provide a reference for the new measurements. This thesis focuses on the improvement and the implementation of TRRL-type high speed profilometers. In order to enable faster and safer operations on the highways, the geometric design of the original system is changed to make it more compact. Using a more compact design increases the observed pitch angles on the system and the increased angles are expected to introduce a new source of error for the measurements. The thesis investigates the effects of vehicle pitching on the measured profiles, and it suggests a method for pitch angle estimation in order to correct the laser measurements. The error analysis seen in the original work is extended and the algorithm is adjusted for the new geometric design. The factor that affects the measurement accuracy the most is determined to be the surface texture due to their randomness. The performance of the originally suggested method for eliminating the texture-caused errors is observed to be insufficient. Therefore, the thesis proposes a new method with a better performance which eliminates the texture-caused errors by modelling them with quadratic functions. The overall performance of the presented profilometer is evaluated by conducting experiments on a road with a known true profile. The accuracy and the repeatability of the observed results indicate that the developed profilometer can be used for measuring true profiles with some further improvement.

    @MastersThesis{ 2015kilic,
    abstract  = {Maintenance of the highway networks is considered to be
    vital in order to ensure transportation safety and quality.
    Within different quality criterions used for a highway,
    road roughness stands out as the most important factor
    which deteriorates the driving comfort and endangers the
    passengers. High-speed profilometers are developed in order
    to be able to monitor road roughness on the highways
    without affecting the normal traffic flow. These devices
    record road elevation profiles while driving at highways
    speeds.
    Transportation and Road Research Laboratory (TRRL) has
    developed a unique high- speed profilometer system. Their
    design contains four laser displacement transducers which
    are placed along a long trailer to be towed by a vehicle.
    As the trailer moves forward, the leading sensor measures
    the road elevation and the other sensors provide a
    reference for the new measurements.
    This thesis focuses on the improvement and the
    implementation of TRRL-type high speed profilometers. In
    order to enable faster and safer operations on the
    highways, the geometric design of the original system is
    changed to make it more compact. Using a more compact
    design increases the observed pitch angles on the system
    and the increased angles are expected to introduce a new
    source of error for the measurements. The thesis
    investigates the effects of vehicle pitching on the
    measured profiles, and it suggests a method for pitch angle
    estimation in order to correct the laser measurements. The
    error analysis seen in the original work is extended and
    the algorithm is adjusted for the new geometric design. The
    factor that affects the measurement accuracy the most is
    determined to be the surface texture due to their
    randomness. The performance of the originally suggested
    method for eliminating the texture-caused errors is
    observed to be insufficient. Therefore, the thesis proposes
    a new method with a better performance which eliminates the
    texture-caused errors by modelling them with quadratic
    functions.
    The overall performance of the presented profilometer is
    evaluated by conducting experiments on a road with a known
    true profile. The accuracy and the repeatability of the
    observed results indicate that the developed profilometer
    can be used for measuring true profiles with some further
    improvement.},
    address  = {Grantham-Allee 20, 53757 St. Augustin, Germany},
    annote  = {WS12 measX GmbH & Co. KG - BAST (High-Speed Profilometer)
    Ploeger, Breuer, Hilsmann supervising},
    author  = {Furkan Kilic},
    month = {January},
    school  = {Bonn-Rhein-Sieg University of Applied Sciences},
    title = {Application and improvement of the TRRL (Transport and
    Road Research Laboratory) high-speed laser profilometer
    algorithm with sensor fusion},
    year = {2015}
    }

  • D. Hernandez, “Robust Localization and Path-tracking for a Mobile Outdoor Robot,” Master Thesis, Grantham-Allee 20, 53757 St. Augustin, Germany, 2015.
    [BibTeX] [Abstract]

    The aim of this master’s thesis is the development of a robust localization and path-tracking system for mobile outdoor robots. The system is implemented in the context of a mobile robot as an assistive device. The developed localization system is suitable for robots with applicability in pedestrian and urban areas, running along walking or bicycles paths. The algorithm is an implementation of a particle filter framework. Data from low-cost GPS, odometry sensors, digital maps and the novelty of a visual road-detection algorithm are fused to estimate robot’s location. The results show a consistent estimated robot’s location with a close-loop error of about 1 meter of accuracy. The robustness of the approach is demonstrated by showing experimental results containing different map configurations highlighting the weaknesses of low-cost GPS and a good algorithm performance even with the unavailability of some of the data.

    @MastersThesis{ 2015hernandez,
    abstract  = {The aim of this master's thesis is the development of a
    robust localization and path-tracking system for mobile
    outdoor robots. The system is implemented in the context of
    a mobile robot as an assistive device. The developed
    localization system is suitable for robots with
    applicability in pedestrian and urban areas, running along
    walking or bicycles paths. The algorithm is an
    implementation of a particle filter framework. Data from
    low-cost GPS, odometry sensors, digital maps and the
    novelty of a visual road-detection algorithm are fused to
    estimate robot's location. The results show a consistent
    estimated robot's location with a close-loop error of about
    1 meter of accuracy. The robustness of the approach is
    demonstrated by showing experimental results containing
    different map configurations highlighting the weaknesses of
    low-cost GPS and a good algorithm performance even with the
    unavailability of some of the data.},
    address  = {Grantham-Allee 20, 53757 St. Augustin, Germany},
    annote  = {WS12/13 Locomotec - RUFUS Prassler, Asteroth, Blumenthal},
    author  = {David Hernandez},
    month = {June},
    school  = {Bonn-Rhein-Sieg University of Applied Sciences},
    title = {Robust Localization and Path-tracking for a Mobile Outdoor
    Robot},
    year = {2015}
    }

2014

  • M. Valdenegro, “Fast Text Detection for Road Scenes,” Master Thesis, Grantham-Allee 20, 53757 St. Augustin, Germany, 2014.
    [BibTeX] [Abstract]

    Extraction of text information from visual sources is an important component of many modern applications, for example, extracting the text from traffic signs on a road scene in an autonomous vehicle. For natural images or road scenes this is a unsolved problem. In this thesis the use of histogram of stroke widths (HSW) for character and non-character region classification is presented. Stroke widths are extracted using two methods. One is based on the Stroke Width Transform and another based on run lengths. The HSW is combined with two simple region features– aspect and occupancy ratios– and then a linear SVM is used as classifier. One advantage of our method over the state of the art is that it is script-independent and can also be used to verify detected text regions with the purpose of reducing false positives. Our experiments on generated datasets of Latin, CJK, Hiragana and Katakana characters show that the HSW is able to correctly classify at least 90 % of the character regions, a similar figure is obtained for non-character regions. This performance is also obtained when training the HSW with one script and testing with a different one, and even when characters are rotated. On the English and Kannada portions of the Chars74K dataset we obtained over 95% correctly classified character regions. The use of raycasting for text line grouping is also proposed. By combining it with our HSW-based character classifier, a text detector based on Maximally Stable Extremal Regions (MSER) was implemented. The text detector was evaluated on our own dataset of road scenes from the German Autobahn, where 65% precision, 72% recall with a f-score of 69% was obtained. Using the HSW as a text verifier increases precision while slightly reducing recall. Our HSW feature allows the building of a script-independent and low parameter count classifier for character and non-character regions

    @MastersThesis{ 2014valdenegro,
    abstract  = {Extraction of text information from visual sources is an
    important component of many modern applications, for
    example, extracting the text from traffic signs on a road
    scene in an autonomous vehicle. For natural images or road
    scenes this is a unsolved problem. In this thesis the use
    of histogram of stroke widths (HSW) for character and
    non-character region classification is presented. Stroke
    widths are extracted using two methods. One is based on the
    Stroke Width Transform and another based on run lengths.
    The HSW is combined with two simple region features–
    aspect and occupancy ratios– and then a linear SVM is
    used as classifier. One advantage of our method over the
    state of the art is that it is script-independent and can
    also be used to verify detected text regions with the
    purpose of reducing false positives. Our experiments on
    generated datasets of Latin, CJK, Hiragana and Katakana
    characters show that the HSW is able to correctly classify
    at least 90 % of the character regions, a similar figure is
    obtained for non-character regions. This performance is
    also obtained when training the HSW with one script and
    testing with a different one, and even when characters are
    rotated. On the English and Kannada portions of the
    Chars74K dataset we obtained over 95% correctly classified
    character regions. The use of raycasting for text line
    grouping is also proposed. By combining it with our
    HSW-based character classifier, a text detector based on
    Maximally Stable Extremal Regions (MSER) was implemented.
    The text detector was evaluated on our own dataset of road
    scenes from the German Autobahn, where 65% precision, 72%
    recall with a f-score of 69% was obtained. Using the HSW as
    a text verifier increases precision while slightly reducing
    recall. Our HSW feature allows the building of a
    script-independent and low parameter count classifier for
    character and non-character regions},
    address  = {Grantham-Allee 20, 53757 St. Augustin, Germany},
    annote  = {WS2012 - Fraunhofer IAIS Pl{\"o}ger, Kraetzschmar,
    Eickeler supervising},
    author  = {Matias Valdenegro},
    month = {September},
    school  = {Bonn-Rhein-Sieg University of Applied Sciences},
    title = {Fast Text Detection for Road Scenes},
    year = {2014}
    }

  • R. K. Venkat, “Smart Person Counter in Mid-Ranging Environments,” Master Thesis, Grantham-Allee 20, 53757 St. Augustin, Germany, 2014.
    [BibTeX] [Abstract]

    Growing usage of train networks is pushing this vital public transport infrastructure to its physical capacity limitations in major cities globally. Capacity planning and people re-routing is increasingly becoming ubiquitous now a days to manage such increasingly crowded environments. With the current trends of growing technology, robotics and automation is an attractive option for solving this problem. A logical starting point to address the task of capacity planning is to understand the extent of the issue: for exam- ple, building automated algorithm for counting the number of people in such scenarios. Keeping this in mind, this thesis concentrates on achieving a robust people counting sys- tem which can handle real world scenarios posing very intricate and challenging situations. A few of the challenging situations are tracking multiple people walking across the sensor’s field of view(FOV) and counting them correctly within an acceptable time for the system to work in real time, tracking people moving very close to each other, tracking people with a variety of walking behaviors and tracking people walking past obstacles such as pillars etc. Out of the above mentioned challenges, this thesis mainly concentrates on the below two problems: 1. Counting multiple people (8-10 in number appearing simultaneously in a single ob- servation), in real time. 10 is approximately the maximum number of people which the primeSense sensor’s FOV can accommodate in a single observation within its working range. 2. Tracking closely moving people, in which case their corresponding blobs merge and the system no longer detects them belonging to two people. The first problem of counting multiple people appearing simultaneously in real time is handled by building the person counting system on top of already existing person detection systems and an existing particle filter. A background image subtraction step is added to improve the people detection rate. The latter problem of closely moving people is handled using an event graph-based approach with a step of identity mapping for which our approach uses the profile shape of people’s head and shoulders which has been shown to be useful in identifying individuals. The evaluation results show that the person counting system is robust enough to handle 8-10 people walking across the FOV in a single observation with an average execu- tion time of 240 ms and a maximum (worst case) execution time of 470 ms. Additionally, the system is robust to scenarios where people walk very closely to each other, maintaining an accurate person count. An empirical evaluation of the approach in a major Sydney public train station (N=522), and results demonstrating the methods in the complexities of this challenging environment are also presented. Furthermore, these results demon- strate that the novel methods contribute significantly to the person counting system, are real world viable and hence lay a foundation for the idea of people congestion awareness towards the goal of achieving efficient capacity planning.

    @MastersThesis{ 2014venkat,
    abstract  = {Growing usage of train networks is pushing this vital
    public transport infrastructure to its physical capacity
    limitations in major cities globally. Capacity planning and
    people re-routing is increasingly becoming ubiquitous now a
    days to manage such increasingly crowded environments. With
    the current trends of growing technology, robotics and
    automation is an attractive option for solving this
    problem. A logical starting point to address the task of
    capacity planning is to understand the extent of the issue:
    for exam- ple, building automated algorithm for counting
    the number of people in such scenarios. Keeping this in
    mind, this thesis concentrates on achieving a robust people
    counting sys- tem which can handle real world scenarios
    posing very intricate and challenging situations. A few of
    the challenging situations are tracking multiple people
    walking across the sensor's field of view(FOV) and counting
    them correctly within an acceptable time for the system to
    work in real time, tracking people moving very close to
    each other, tracking people with a variety of walking
    behaviors and tracking people walking past obstacles such
    as pillars etc. Out of the above mentioned challenges, this
    thesis mainly concentrates on the below two problems: 1.
    Counting multiple people (8-10 in number appearing
    simultaneously in a single ob- servation), in real time. 10
    is approximately the maximum number of people which the
    primeSense sensor's FOV can accommodate in a single
    observation within its working range. 2. Tracking closely
    moving people, in which case their corresponding blobs
    merge and the system no longer detects them belonging to
    two people. The first problem of counting multiple people
    appearing simultaneously in real time is handled by
    building the person counting system on top of already
    existing person detection systems and an existing particle
    filter. A background image subtraction step is added to
    improve the people detection rate. The latter problem of
    closely moving people is handled using an event graph-based
    approach with a step of identity mapping for which our
    approach uses the profile shape of people's head and
    shoulders which has been shown to be useful in identifying
    individuals. The evaluation results show that the person
    counting system is robust enough to handle 8-10 people
    walking across the FOV in a single observation with an
    average execu- tion time of 240 ms and a maximum (worst
    case) execution time of 470 ms. Additionally, the system is
    robust to scenarios where people walk very closely to each
    other, maintaining an accurate person count. An empirical
    evaluation of the approach in a major Sydney public train
    station (N=522), and results demonstrating the methods in
    the complexities of this challenging environment are also
    presented. Furthermore, these results demon- strate that
    the novel methods contribute significantly to the person
    counting system, are real world viable and hence lay a
    foundation for the idea of people congestion awareness
    towards the goal of achieving efficient capacity planning.
    },
    address  = {Grantham-Allee 20, 53757 St. Augustin, Germany},
    annote  = {SS11 FH-BRS - UTS,Sydney Pl{\"o}ger, Kraetzschmar,
    Kirchner supervising},
    author  = {Ravi Kumar Venkat},
    month = {May},
    school  = {Bonn-Rhein-Sieg University of Applied Sciences},
    title = {Smart Person Counter in Mid-Ranging Environments},
    year = {2014}
    }

  • Pramanujam, “Robust Navigation of a Mobile Manipulator in a Dynamic Environment,” Master Thesis, Grantham-Allee 20, 53757 St. Augustin, Germany, 2014.
    [BibTeX] [Abstract]

    Obstacle avoidance is one of the essential components to achieve autonomy in robot navigation. Algorithms for obstacle avoidance have been developed for over past two decades and the recent advances made in these algorithms do not only deal static but also dynamic obstacles. However, in the case of highly cluttered environments, these algorithms stop navigating the robot and wait for the environment to clear, which is undesirable in crowded environments. Another situation during which these algorithms break is when the external sensors fail or are unable to perceive the environment. The condition of the robot in such scenarios is similar to a human navigating in the dark. The thesis is motivated by the above arguments and addresses the problem similar to how human copes up with trying to find a path in a heavily crowded environment or in the dark. The sense of touch and pressure is used to recover from such scenarios. In order to achieve this, the concept of \emph{compliance} has been adapted from the field of robot manipulation and applied to robot navigation, whereby the collision is considered as an external force being exerted on the system. The thesis proposes a coherent framework to combine compliance in navigation to the existing navigation algorithms, thereby, allowing robots to deal with real as well as virtual forces of the environment. In order to realize the framework, kinetic analysis on a four wheeled omni-directional robot was performed. As a result, the robot can be torque controlled given a certain path to be followed. This provides an estimate of the force exerted by the robot for its motion. A disturbance observer based on robot’s momentum was implemented in order to detect undesirable collisions. The experiments show encouraging results for detecting obstacles at low velocity.

    @MastersThesis{ 2014ramanujam,
    abstract  = {Obstacle avoidance is one of the essential components to
    achieve autonomy in robot navigation. Algorithms for
    obstacle avoidance have been developed for over past two
    decades and the recent advances made in these algorithms do
    not only deal static but also dynamic obstacles. However,
    in the case of highly cluttered environments, these
    algorithms stop navigating the robot and wait for the
    environment to clear, which is undesirable in crowded
    environments. Another situation during which these
    algorithms break is when the external sensors fail or are
    unable to perceive the environment. The condition of the
    robot in such scenarios is similar to a human navigating in
    the dark.
    The thesis is motivated by the above arguments and
    addresses the problem similar to how human copes up with
    trying to find a path in a heavily crowded environment or
    in the dark. The sense of touch and pressure is used to
    recover from such scenarios. In order to achieve this, the
    concept of \emph{compliance} has been adapted from the
    field of robot manipulation and applied to robot
    navigation, whereby the collision is considered as an
    external force being exerted on the system.
    The thesis proposes a coherent framework to combine
    compliance in navigation to the existing navigation
    algorithms, thereby, allowing robots to deal with real as
    well as virtual forces of the environment. In order to
    realize the framework, kinetic analysis on a four wheeled
    omni-directional robot was performed. As a result, the
    robot can be torque controlled given a certain path to be
    followed. This provides an estimate of the force exerted by
    the robot for its motion. A disturbance observer based on
    robot's momentum was implemented in order to detect
    undesirable collisions. The experiments show encouraging
    results for detecting obstacles at low velocity. },
    address  = {Grantham-Allee 20, 53757 St. Augustin, Germany},
    annote  = {SS11 FH-BRS Pl{\"o}ger, Prassler, Blumenthal},
    author  = {Pramanujam},
    month = {August},
    school  = {Bonn-Rhein-Sieg University of Applied Sciences},
    title = {Robust Navigation of a Mobile Manipulator in a Dynamic
    Environment },
    year = {2014}
    }

  • S. Junoh, “Development of a Cognitive UAV for Medical Assistance Application:Integration of a Soar Agent in ROS,” Master Thesis, Grantham-Allee 20, 53757 St. Augustin, Germany, 2014.
    [BibTeX] [Abstract]

    A scenario where a set of small-scale UAVs is used to deliver medical goods to assist injured or sick people in remote locations is proposed. A typical example would be a person having been bitten by a snake in a remote area and desperately needing snake antivenom. The UAV emergency system would then prepare a UAV with the snake antivenom and the UAV would then y to the injured person and deliver the medicine. As this medical assistance system should operate at all times and would often be based in remote locations, it is only feasible when it involves minimum human interaction. This is one of perfect scenarios by which UAV system benefits from a high degree of autonomy. The UAV has to handle the delivery of the medicine even in adverse conditions without relying on human intervention. Fortunately, over the past decades, there has been plenty of progress in the research of autonomy allowing us nowadays to design vehicles with an ever increasing degree of autonomy. One research effort in the eld of autonomy is cognitive architectures, with Soar being among the prominent. Soar tries to model the human cognitive process and ability in a software architecture. Their eorts (since 1983) resulted in a exible cognitive architecture which is available as Open Source software. In this thesis, the use of Soar in the context of the medical assistance UAV is investigated. While all autonomy functionalities are handled by the Soar framework, general capabilities and the middleware of the UAV are modeled using the widely used robot framework ROS. ROS has been providing many modules which allow for ying and navigating small-scale UAVs and therefore enabling a fast prototype development of the UAV software. In this thesis, both the system and the cognitive functionalities of the autonomous medical assistance UAV are designed. Consequently, a major part of this thesis is the integration of Soar into ROS (ROSied Soar). Starting from an overall architecture design including the UAV systems and the cognitive agent, a software implementation was derived. Finally, the UAV model was tested in a simulation environment (ROS/Gazebo) where the UAV performed a delivery mission. The simulation included the straightforward regular delivery as well as missions where stressors were applied which forced the Soar agent to react and make alternative decisions. Preliminary simulation data reveal that this approach has the potential for creating such a medical assistance UAV system. This also means that the synergy between Soar and ROS has been achieved, hence, has shown the usefulness of this integration for the use of UAVs deployed for complex mission.

    @MastersThesis{ 2014junoh,
    abstract  = {A scenario where a set of small-scale UAVs is used to
    deliver medical goods to assist injured or sick people in
    remote locations is proposed. A typical example would be a
    person having been bitten by a snake in a remote area and
    desperately needing snake antivenom. The UAV emergency
    system would then prepare a UAV with the snake antivenom
    and the UAV would then y to the injured person and deliver
    the medicine. As this medical assistance system should
    operate at all times and would often be based in remote
    locations, it is only feasible when it involves minimum
    human interaction. This is one of perfect scenarios by
    which UAV system benefits from a high degree of autonomy.
    The UAV has to handle the delivery of the medicine even in
    adverse conditions without relying on human intervention.
    Fortunately, over the past decades, there has been plenty
    of progress in the research of autonomy allowing us
    nowadays to design vehicles with an ever increasing degree
    of autonomy. One research effort in the eld of autonomy is
    cognitive architectures, with Soar being among the
    prominent. Soar tries to model the human cognitive process
    and ability in a software architecture. Their eorts (since
    1983) resulted in a exible cognitive architecture which is
    available as Open Source software. In this thesis, the use
    of Soar in the context of the medical assistance UAV is
    investigated. While all autonomy functionalities are
    handled by the Soar framework, general capabilities and the
    middleware of the UAV are modeled using the widely used
    robot framework ROS. ROS has been providing many modules
    which allow for ying and navigating small-scale UAVs and
    therefore enabling a fast prototype development of the UAV
    software. In this thesis, both the system and the cognitive
    functionalities of the autonomous medical assistance UAV
    are designed. Consequently, a major part of this thesis is
    the integration of Soar into ROS (ROSied Soar). Starting
    from an overall architecture design including the UAV
    systems and the cognitive agent, a software implementation
    was derived. Finally, the UAV model was tested in a
    simulation environment (ROS/Gazebo) where the UAV performed
    a delivery mission. The simulation included the
    straightforward regular delivery as well as missions where
    stressors were applied which forced the Soar agent to react
    and make alternative decisions. Preliminary simulation data
    reveal that this approach has the potential for creating
    such a medical assistance UAV system. This also means that
    the synergy between Soar and ROS has been achieved, hence,
    has shown the usefulness of this integration for the use of
    UAVs deployed for complex mission.},
    address  = {Grantham-Allee 20, 53757 St. Augustin, Germany},
    annote  = {SS09 Silver Atena Electronic Systems Engineering GmbH -
    Autonomy Kreatzschmar, Heni, Stenger supervising},
    author  = {Shahmi Junoh},
    month = {November},
    school  = {Bonn-Rhein-Sieg University of Applied Sciences},
    title = {Development of a Cognitive UAV for Medical Assistance
    Application:Integration of a Soar Agent in ROS},
    year = {2014}
    }

  • N. Giftsun, “‘Stack of Tasks’ Controller for Mobile Manipulators with a Path Planner,” Master Thesis, Grantham-Allee 20, 53757 St. Augustin, Germany, 2014.
    [BibTeX] [Abstract]

    Mobile Manipulators are usually redundant systems providing the flexibility of handling task space constraints. Prioritized Task Function based control schemes are quite popu- lar among the community of Humanoid Robots equipped with five times more than the degrees of freedom in a Mobile Manipulator. These control schemes don’t require a closed loop inverse kinematic solution and can avoid conflicts between multiple tasks. ‘Stack of Tasks'(SOT) is one such framework supported by state-of-the-art solver to handle equality and inequality constraints much efficiently. Though the task function based control strat- egy are quite attractive in terms of handling multiple tasks, they are only locally optimal. The controller needs the help of a global planner in avoiding the local minimum which is inherent in all Jacobian based controllers. This thesis focuses on developing a generic SOT controller for Mobile Manipulators capable of handling both motion-generation and path following tasks in real time scenarios. A study is done on the solver and the planning modules to bridge the planning component and the SOT controller. The performance of the controller is evaluated by experiments both in simulation and real PR2 robot.

    @MastersThesis{ 2014giftsun,
    abstract  = {Mobile Manipulators are usually redundant systems
    providing the flexibility of handling task space
    constraints. Prioritized Task Function based control
    schemes are quite popu- lar among the community of Humanoid
    Robots equipped with five times more than the degrees of
    freedom in a Mobile Manipulator. These control schemes
    don't require a closed loop inverse kinematic solution and
    can avoid conflicts between multiple tasks. 'Stack of
    Tasks'(SOT) is one such framework supported by
    state-of-the-art solver to handle equality and inequality
    constraints much efficiently. Though the task function
    based control strat- egy are quite attractive in terms of
    handling multiple tasks, they are only locally optimal. The
    controller needs the help of a global planner in avoiding
    the local minimum which is inherent in all Jacobian based
    controllers. This thesis focuses on developing a generic
    SOT controller for Mobile Manipulators capable of handling
    both motion-generation and path following tasks in real
    time scenarios. A study is done on the solver and the
    planning modules to bridge the planning component and the
    SOT controller. The performance of the controller is
    evaluated by experiments both in simulation and real PR2
    robot.},
    address  = {Grantham-Allee 20, 53757 St. Augustin, Germany},
    annote  = {SS2011 LAAS-CNRS - Factory in a Day Ploeger, Lamiraux,
    Kahl supervising},
    author  = {Nirmal Giftsun},
    month = {October},
    school  = {Bonn-Rhein-Sieg University of Applied Sciences},
    title = {‘Stack of Tasks' Controller for Mobile Manipulators with
    a Path Planner},
    year = {2014}
    }

  • T. C. Hassan, “Dynamic Facial Expression Estimation by means of Model Fitting,” Master Thesis, Grantham-Allee 20, 53757 St. Augustin, Germany, 2014.
    [BibTeX] [Abstract]

    Analysis of facial expressions is an integral part of human behavioural research. The Facial Action Coding System (FACS) manual guides researchers in identifying and coding facial expressions in terms of basic facial movements called Action Units (AUs). However, coding faces manually, based on FACS, is a tedious process. Automating the FACS-based analysis of faces in images and image sequences would save a great amount of time, and thereby accelerate behavioural research. Automatic facial AU analysis would also be of value in developing technologies for affect-based human-computer (robot) interaction. This thesis deals with the problem of fully automatic estimation of AUs in image sequences. A model-based approach is pursued. The shape of the human face is represented in the form of an interconnected mesh of vertices. Linear models describe the shape in terms of deformation vectors controlled by a set of parameters. These deformation vectors correspond to changes in facial shape resulting from individual AUs. The parameters that control these deformations denote the intensity at which the AUs are expressed. Existing methods for model fitting can be used to determine the AU model parameters. However, these methods follow a frame-by-frame strategy and do not incorporate the dynamics of underlying motion. This causes two main problems. Firstly, the trajectories of estimated parameters are noisy. Secondly, ambiguities in AU parameter estimates cannot be resolved correctly. As a result, the AU estimation performance is poor. State estimation methods allow dynamic models of parameter evolution to be combined with noisy observations of textures or model vertices given by the model-fitting methods. In this thesis, the use of state estimation methods to improve AU estimation performance is investigated.

    @MastersThesis{ 2014hassan,
    abstract  = {Analysis of facial expressions is an integral part of
    human behavioural research. The Facial Action Coding System
    (FACS) manual guides researchers in identifying and coding
    facial expressions in terms of basic facial movements
    called Action Units (AUs). However, coding faces manually,
    based on FACS, is a tedious process. Automating the
    FACS-based analysis of faces in images and image sequences
    would save a great amount of time, and thereby accelerate
    behavioural research. Automatic facial AU analysis would
    also be of value in developing technologies for
    affect-based human-computer (robot) interaction. This
    thesis deals with the problem of fully automatic estimation
    of AUs in image sequences. A model-based approach is
    pursued. The shape of the human face is represented in the
    form of an interconnected mesh of vertices. Linear models
    describe the shape in terms of deformation vectors
    controlled by a set of parameters. These deformation
    vectors correspond to changes in facial shape resulting
    from individual AUs. The parameters that control these
    deformations denote the intensity at which the AUs are
    expressed. Existing methods for model fitting can be used
    to determine the AU model parameters. However, these
    methods follow a frame-by-frame strategy and do not
    incorporate the dynamics of underlying motion. This causes
    two main problems. Firstly, the trajectories of estimated
    parameters are noisy. Secondly, ambiguities in AU parameter
    estimates cannot be resolved correctly. As a result, the AU
    estimation performance is poor. State estimation methods
    allow dynamic models of parameter evolution to be combined
    with noisy observations of textures or model vertices given
    by the model-fitting methods. In this thesis, the use of
    state estimation methods to improve AU estimation
    performance is investigated.},
    address  = {Grantham-Allee 20, 53757 St. Augustin, Germany},
    annote  = {SS12 Fraunhofer IIS Prassler, Pl\"{o}ger, F\"{u}ller,
    Seuss supervising},
    author  = {Teena Chakkalayil Hassan},
    month = {June},
    school  = {Bonn-Rhein-Sieg University of Applied Sciences},
    title = {Dynamic Facial Expression Estimation by means of Model
    Fitting},
    year = {2014}
    }

  • O. Alaqtash, “Creating a Focused HTN Planning Problem using DL Inferencing,” Master Thesis, Grantham-Allee 20, 53757 St. Augustin, Germany, 2014.
    [BibTeX] [Abstract]

    Automated planning allows systems to automatically generate plans in order to execute tasks or achieve goals. To accomplish this, planners use knowledge of the actions that can be performed, when they can be carried out as well as how these actions affect the world. This knowledge represents the planning domain. Moreover, planners need a description of the world. A planning problem consists of these two items as well as the task to be achieved. Hierarchical Task Network (HTN) planning is a popular automated planning approach. It encodes domain information that defines recipes on how to carry out a task. This results in a pruned search space and provides quality plans. However, the search space remains large and this can lead to intractability. Many real-world planning problems are still difficult to solve in a reasonable time by autonomous agents. The reason for this is that the planning problems are not preprocessed to include only the relevant parts of the domain and the description of the world. This preprocessing is carried out by a human for systems without full autonomy. Researchers continue to optimize planning approaches and planners, but very few have tackled the problem of identifying relevant parts of the planning problem. The work of Hartanto (2011) is among the first to propose a solution which involves modeling the planning domain in the Web Ontology Language (OWL). However, his solution has a number of deficits. Most importantly, his modeling of the planning domain is not flexible enough to be reused. In this work, we adopt Hartanto’s approach of representing the planning domain in OWL to enable inference and create a focused planning problem. To do this, we model the planning domain and the description of the world in a decidable fragment of OWL, namely OWL 2 DL, and create a component that queries this knowledge by generating a set of SPARQL-DL queries to identify the relevant parts of the description of the world and the domain. The solution is able to generate a focused planning problem in OWL 2 DL, which can later be transformed into the JSHOP2 syntax for the planner to use. The results of our experiments showed a reduction of between 58% to 81% from the original size of the planning problem. Such a decrease in the problem size should lead to a much faster plan generation process.

    @MastersThesis{ 2014alaqtash,
    abstract  = {Automated planning allows systems to automatically
    generate plans in order to execute tasks or achieve goals.
    To accomplish this, planners use knowledge of the actions
    that can be performed, when they can be carried out as well
    as how these actions affect the world. This knowledge
    represents the planning domain. Moreover, planners need a
    description of the world. A planning problem consists of
    these two items as well as the task to be achieved.
    Hierarchical Task Network (HTN) planning is a popular
    automated planning approach. It encodes domain information
    that defines recipes on how to carry out a task. This
    results in a pruned search space and provides quality
    plans. However, the search space remains large and this can
    lead to intractability. Many real-world planning problems
    are still difficult to solve in a reasonable time by
    autonomous agents. The reason for this is that the planning
    problems are not preprocessed to include only the relevant
    parts of the domain and the description of the world. This
    preprocessing is carried out by a human for systems without
    full autonomy. Researchers continue to optimize planning
    approaches and planners, but very few have tackled the
    problem of identifying relevant parts of the planning
    problem. The work of Hartanto (2011) is among the first to
    propose a solution which involves modeling the planning
    domain in the Web Ontology Language (OWL). However, his
    solution has a number of deficits. Most importantly, his
    modeling of the planning domain is not flexible enough to
    be reused. In this work, we adopt Hartanto's approach of
    representing the planning domain in OWL to enable inference
    and create a focused planning problem. To do this, we model
    the planning domain and the description of the world in a
    decidable fragment of OWL, namely OWL 2 DL, and create a
    component that queries this knowledge by generating a set
    of SPARQL-DL queries to identify the relevant parts of the
    description of the world and the domain. The solution is
    able to generate a focused planning problem in OWL 2 DL,
    which can later be transformed into the JSHOP2 syntax for
    the planner to use. The results of our experiments showed a
    reduction of between 58% to 81% from the original size of
    the planning problem. Such a decrease in the problem size
    should lead to a much faster plan generation process.},
    address  = {Grantham-Allee 20, 53757 St. Augustin, Germany},
    annote  = {WS12/13 H-BRS - Creating a Focused HTN Planning Problem
    using DL Inferencing Kr{\"a}tzschmar, Pl{\"o}ger, Awaad
    supervising},
    author  = {Obada Alaqtash},
    month = {December},
    school  = {Bonn-Rhein-Sieg University of Applied Sciences},
    title = {Creating a Focused HTN Planning Problem using DL
    Inferencing},
    year = {2014}
    }

2013

  • M. W. Butt, “A computational visual attention system for diver’s hand signs and gestures recognition,” Master Thesis, Grantham Allee 20, 53757 St. Augustin, Germany, 2013.
    [BibTeX]
    @MastersThesis{ butt2013a-computational,
    address  = {Grantham Allee 20, 53757 St. Augustin, Germany},
    annote  = {[2013] [Fintrope], [Ploeger] supervising},
    author  = {Mohsin Wahab Butt},
    date-added  = {2016-08-28 08:11:03 +0000},
    date-modified  = {2016-08-28 08:13:31 +0000},
    month = {August},
    school  = {Bonn-Rhein-Sieg University of Applied Sciences},
    title = {A computational visual attention system for diver's hand
    signs and gestures recognition},
    year = {2013}
    }

  • S. Schneider, “Design of a declarative language for task-oriented grasping and tool-use with dextrous robotic hands,” Master Thesis, Grantham-Allee 20, 53757 St. Augustin, Germany, 2013.
    [BibTeX] [Abstract]

    Apparently simple manipulation tasks for a human such as transportation or tool use are challenging to replicate in an autonomous service robot. Nevertheless, dextrous ma- nipulation is an important aspect for a robot in many daily tasks. While it is possible to manufacture special-purpose hands for one specific task in industrial settings, a general- purpose service robot in households must have flexible hands which can adapt to many tasks. Intelligently using tools enables the robot to perform tasks more efficiently and even beyond the designed capabilities. In this work a declarative domain-specific language, called Grasp Domain Definition Language (GDDL), is presented that allows the specification of grasp planning problems independently of a specific grasp planner. This design goal resembles the idea of the Planning Domain Definition Language (PDDL). The specification of GDDL requires a detailed analysis of the research in grasping in order to identify best practices in different domains that contribute to a grasp. These domains describe for instance physical as well as semantic properties of objects and hands. Grasping always has a purpose which is captured in the task domain definition. It enables the robot to grasp an object in a task- dependent manner. Suitable representations in these domains have to be identified and formalized for which a domain-driven software engineering approach is applied. This kind of modeling allows the specification of constraints which guide the composition of domain entity specifications. The domain-driven approach fosters reuse of domain concepts while the constraints enable the validation of models already during design time. A proof of concept implementation of GDDL into the GraspIt! grasp planner is developed. Preliminary results of this thesis have been published and presented on the IEEE International Conference on Robotics and Automation (ICRA).

    @MastersThesis{ 2013schneider,
    abstract  = {Apparently simple manipulation tasks for a human such as
    transportation or tool use are challenging to replicate in
    an autonomous service robot. Nevertheless, dextrous ma-
    nipulation is an important aspect for a robot in many daily
    tasks. While it is possible to manufacture special-purpose
    hands for one specific task in industrial settings, a
    general- purpose service robot in households must have
    flexible hands which can adapt to many tasks. Intelligently
    using tools enables the robot to perform tasks more
    efficiently and even beyond the designed capabilities. In
    this work a declarative domain-specific language, called
    Grasp Domain Definition Language (GDDL), is presented that
    allows the specification of grasp planning problems
    independently of a specific grasp planner. This design goal
    resembles the idea of the Planning Domain Definition
    Language (PDDL). The specification of GDDL requires a
    detailed analysis of the research in grasping in order to
    identify best practices in different domains that
    contribute to a grasp. These domains describe for instance
    physical as well as semantic properties of objects and
    hands. Grasping always has a purpose which is captured in
    the task domain definition. It enables the robot to grasp
    an object in a task- dependent manner. Suitable
    representations in these domains have to be identified and
    formalized for which a domain-driven software engineering
    approach is applied. This kind of modeling allows the
    specification of constraints which guide the composition of
    domain entity specifications. The domain-driven approach
    fosters reuse of domain concepts while the constraints
    enable the validation of models already during design time.
    A proof of concept implementation of GDDL into the GraspIt!
    grasp planner is developed. Preliminary results of this
    thesis have been published and presented on the IEEE
    International Conference on Robotics and Automation
    (ICRA).},
    address  = {Grantham-Allee 20, 53757 St. Augustin, Germany},
    note = {WS09/10 H-BRS - RoboCup Kraetzschmar, Pl{\"o}ger,
    Hochgeschwender},
    author  = {Sven Schneider},
    month = {May},
    school  = {Bonn-Rhein-Sieg University of Applied Sciences},
    title = {Design of a declarative language for task-oriented
    grasping and tool-use with dextrous robotic hands},
    year = {2013}
    }

  • F. Rouatbi, “Two SVM-based methods for the classification of airborne LiDAR data,” Master Thesis, Grantham-Allee 20, 53757 St. Augustin, Germany, 2013.
    [BibTeX] [Abstract]

    Airborne Light Detection And Ranging (LiDAR) is a remote sensing method used to collect high resolution information of the earth’s surface. This technology uses laser light beams emitted from a LiDAR system mounted on an airborne platform to scan the landscape topology and the different objects above the bare-earth such as buildings, vegetation, cars and roads. The collected data is an elevation model in the form of a point cloud which is useful for a wide range of applications such as 3D modeling, change detection analysis and objects recognition. In order to extract relevant information from the scanned areas, the generated point cloud needs to be classified. In this thesis, we propose two different methods for the classification of airborne LiDAR data into ground, trees, and buildings. The first method is a point-based classification approach which works on each point independently and uses geometrical features derived from the local neighborhood to make a decision about the class attributes. Therefore, a classification system composed of a cascade of three independent binary classifiers (tree, ground and buildings) has been implemented. Each classifier is based on a support vector machine (SVM) which recognizes the points of a particular class and removes them from the dataset. First, the tree classifier is used to detects the tree points as they have the most discriminating features. Then, the remaining non-tree points are passed to the ground classifier which recognizes the ground points and eliminates them from the dataset. Finally, the building classifier is applied in order to separate the points belonging to the buildings from other small objects which have no predefined class such as cars. The second method is an object-based classification approach which segments the data and performs a classification of of the resulting segments. The surface growing algorithm was used to segment the proximal points having geometrical similarities into a set of disjoint objects. The ground objects are first identified based on the size and the median height of the points in each segment. Then, a SVM-based classifier is used to distinguish the objects corresponding to the buildings. Therefore, a set of object-based features derived from the geometrical attributes of the points within the same segment has been used. Both methods have been tested on a labeled dataset composed of more than one million LiDAR points and applied to a set of real data.

    @MastersThesis{ 2013rouatbi,
    abstract  = {Airborne Light Detection And Ranging (LiDAR) is a remote
    sensing method used to collect high resolution information
    of the earth's surface. This technology uses laser light
    beams emitted from a LiDAR system mounted on an airborne
    platform to scan the landscape topology and the different
    objects above the bare-earth such as buildings, vegetation,
    cars and roads. The collected data is an elevation model in
    the form of a point cloud which is useful for a wide range
    of applications such as 3D modeling, change detection
    analysis and objects recognition. In order to extract
    relevant information from the scanned areas, the generated
    point cloud needs to be classified. In this thesis, we
    propose two different methods for the classification of
    airborne LiDAR data into ground, trees, and buildings. The
    first method is a point-based classification approach which
    works on each point independently and uses geometrical
    features derived from the local neighborhood to make a
    decision about the class attributes. Therefore, a
    classification system composed of a cascade of three
    independent binary classifiers (tree, ground and buildings)
    has been implemented. Each classifier is based on a support
    vector machine (SVM) which recognizes the points of a
    particular class and removes them from the dataset. First,
    the tree classifier is used to detects the tree points as
    they have the most discriminating features. Then, the
    remaining non-tree points are passed to the ground
    classifier which recognizes the ground points and
    eliminates them from the dataset. Finally, the building
    classifier is applied in order to separate the points
    belonging to the buildings from other small objects which
    have no predefined class such as cars. The second method is
    an object-based classification approach which segments the
    data and performs a classification of of the resulting
    segments. The surface growing algorithm was used to segment
    the proximal points having geometrical similarities into a
    set of disjoint objects. The ground objects are first
    identified based on the size and the median height of the
    points in each segment. Then, a SVM-based classifier is
    used to distinguish the objects corresponding to the
    buildings. Therefore, a set of object-based features
    derived from the geometrical attributes of the points
    within the same segment has been used. Both methods have
    been tested on a labeled dataset composed of more than one
    million LiDAR points and applied to a set of real data.},
    address  = {Grantham-Allee 20, 53757 St. Augustin, Germany},
    annote  = {SS08 FH-BRS - Fraunhofer FKIE Two SVM-based methods for
    the classification of airborne LiDAR data Ploeger, Koch
    supervising},
    author  = {Fahmi Rouatbi},
    month = {November},
    school  = {Bonn-Rhein-Sieg University of Applied Sciences},
    title = {Two SVM-based methods for the classification of airborne
    LiDAR data},
    year = {2013}
    }

  • M. Pathare, “Household Device Recognition & Information Retrieval using a Smart-phone,” Master Thesis, Grantham-Allee 20, 53757 St. Augustin, Germany, 2013.
    [BibTeX] [Abstract]

    Present household devices are not like their predecessors, having a single on/off switch, instead they have numerous settings and functionalities. User’s need to have manual of such devices handy. In case they forget which, and how to adjust the device parameters or how to carry out certain ‘not so common tasks’ on the device. Sometimes the device interfaces can be difficult to understand, not only for elders but also for common people. Having the device’s operational information on our finger tips is a major requirement and also the information should be easily accessible. This issue is addressed in our work. A smart-phone can be a very handy tool in such situations. We have developed an application on a smart-phone that can recognize known household devices, retrieve its operational information, and present it to the user in an interactive way. Smaller, vision deprived robots can also benefit from our application. They can outsource the device recognition task to our application and will receive a feedback about the device and its details. Vision based techniques are used to find the device, that user needs information on. Feature base approach is used to recognize the device. The device’s operational information is stored in local database and is retrieved after its successful recognition. This information contains functionality of the controls present on the device and the tasks that can be performed with it. Users can view this information interactively. Robots can access the location information of the recognized device in the scene image. After evaluation, it was found that our application is robust in recognizing devices under illumination, scale and rotation variations and also under slight occlusions. No false positive detections were observed during tests, due to geometrical and text based validation checks. For five devices in database, application takes about 20 seconds to recognize a device, including focusing and image capturing time. The observed mean square error in estimating distance to device and horizontal angle to the device measured from optical axis, were 0.3097cm and 1.393$^\circ$ respectively. Our application proves useful to users, as well as robots in getting to know a device’s operational information. An end-to-end prototype was also developed, showing a robot mounted with a smart-phone running our application, searching for a device, recognizing it, localizing it and grasping it successfully.

    @MastersThesis{ 2013pathare,
    abstract  = {Present household devices are not like their predecessors,
    having a single on/off switch, instead they have numerous
    settings and functionalities. User's need to have manual of
    such devices handy. In case they forget which, and how to
    adjust the device parameters or how to carry out certain
    'not so common tasks' on the device. Sometimes the device
    interfaces can be difficult to understand, not only for
    elders but also for common people.
    Having the device's operational information on our finger
    tips is a major requirement and also the information should
    be easily accessible. This issue is addressed in our work.
    A smart-phone can be a very handy tool in such situations.
    We have developed an application on a smart-phone that can
    recognize known household devices, retrieve its operational
    information, and present it to the user in an interactive
    way. Smaller, vision deprived robots can also benefit from
    our application. They can outsource the device recognition
    task to our application and will receive a feedback about
    the device and its details.
    Vision based techniques are used to find the device, that
    user needs information on. Feature base approach is used to
    recognize the device. The device's operational information
    is stored in local database and is retrieved after its
    successful recognition. This information contains
    functionality of the controls present on the device and the
    tasks that can be performed with it. Users can view this
    information interactively. Robots can access the location
    information of the recognized device in the scene image.
    After evaluation, it was found that our application is
    robust in recognizing devices under illumination, scale and
    rotation variations and also under slight occlusions. No
    false positive detections were observed during tests, due
    to geometrical and text based validation checks. For five
    devices in database, application takes about 20 seconds to
    recognize a device, including focusing and image capturing
    time. The observed mean square error in estimating distance
    to device and horizontal angle to the device measured from
    optical axis, were 0.3097cm and 1.393$^\circ$ respectively.
    Our application proves useful to users, as well as robots
    in getting to know a device's operational information. An
    end-to-end prototype was also developed, showing a robot
    mounted with a smart-phone running our application,
    searching for a device, recognizing it, localizing it and
    grasping it successfully.},
    address  = {Grantham-Allee 20, 53757 St. Augustin, Germany},
    annote  = {WS10 H-BRS Pl{\"o}ger,Breuer supervising},
    author  = {Mandar Pathare},
    month = {April},
    school  = {Bonn-Rhein-Sieg University of Applied Sciences},
    title = {Household Device Recognition & Information Retrieval using
    a Smart-phone},
    year = {2013}
    }

  • Z. B. Kasim, “HMM-Based Diagnosis of Known Exogenous Interventions in Mobile Manipulators,” Master Thesis, Grantham-Allee 20, 53757 St. Augustin, Germany, 2013.
    [BibTeX] [Abstract]

    A mobile manipulator lives in a dynamic environment where changes occur over time. The changes are external, where they occur outside the mobile manipulator’s control, however, they affect the results for a task assigned for the mobile manipulator. These external events are called exogenous interventions. The main purpose of this thesis is to provide a model based, probabilistic method as an approach for known exogenous interventions in mobile manipulators. With the thought of the mobile platform and manipulator can work in parallel, we proposed two variations of continuous observation hidden Markov model, namely the classical hidden Markov model and the parallel hidden Markov model. We elaborated several use cases for exogenous interventions and with these we collected data from real and simulated mobile manipulators. Due the characteristics of exogenous interventions which are rare, random and versatile; we also obtained statistics for exogenous interventions to build up the model. We then tested the models on a simulated environment. The result shows that diagnosing exogenous interventions are precise and sensitive. With correct modeling, the hidden Markov Model is proven to be able to diagnose the exogenous interventions correctly.

    @MastersThesis{ 2013kasim,
    abstract  = {A mobile manipulator lives in a dynamic environment where
    changes occur over time. The changes are external, where
    they occur outside the mobile manipulator's control,
    however, they affect the results for a task assigned for
    the mobile manipulator. These external events are called
    exogenous interventions. The main purpose of this thesis is
    to provide a model based, probabilistic method as an
    approach for known exogenous interventions in mobile
    manipulators. With the thought of the mobile platform and
    manipulator can work in parallel, we proposed two
    variations of continuous observation hidden Markov model,
    namely the classical hidden Markov model and the parallel
    hidden Markov model. We elaborated several use cases for
    exogenous interventions and with these we collected data
    from real and simulated mobile manipulators. Due the
    characteristics of exogenous interventions which are rare,
    random and versatile; we also obtained statistics for
    exogenous interventions to build up the model. We then
    tested the models on a simulated environment. The result
    shows that diagnosing exogenous interventions are precise
    and sensitive. With correct modeling, the hidden Markov
    Model is proven to be able to diagnose the exogenous
    interventions correctly.},
    address  = {Grantham-Allee 20, 53757 St. Augustin, Germany},
    annote  = {SS09 Pl\"oger, von der Hude, K\"ustenmacher supervising},
    author  = {Zinnirah Binti Kasim},
    month = {July},
    school  = {Bonn-Rhein-Sieg University of Applied Sciences},
    title = {HMM-Based Diagnosis of Known Exogenous Interventions in
    Mobile Manipulators},
    year = {2013}
    }

2012

  • T. Mathew, “A Computer Game based Motivation System for Human Muscle Strength Testing,” Master Thesis, Grantham-Allee 20, 53757 St. Augustin, Germany, 2012.
    [BibTeX] [Abstract]

    The objective of this thesis is to implement a computer game based motivation system for maximal strength testing on the Biodex System 3 Isokinetic Dynamometer. The prototype game has been designed to improve the peak torque produced in an isometric knee extensor strength test. An extensive analysis is performed on a torque data set from a previous study. The torque responses for five second long maximal voluny contractions of the knee extensor are analyzed to understand torque response characteristics of di fferent subjects. The parameters identified in the data analysis are used in the implementation of the ‘Shark and School of Fish’ game. The behavior of the game for different torque responses is analyzed on a different torque data set from the previous study. The evaluationshows that the game rewards and motivates continuously over a repetition to reach the peak torque value. The evaluation also shows that the game rewards the user more if he overcomes a baseline torque value within the first second and then gradually increase the torque to reach peak torque.

    @MastersThesis{ 2012mathew,
    abstract  = {The objective of this thesis is to implement a computer
    game based motivation system for maximal strength testing
    on the Biodex System 3 Isokinetic Dynamometer. The
    prototype game has been designed to improve the peak torque
    produced in an isometric knee extensor strength test. An
    extensive analysis is performed on a torque data set from a
    previous study. The torque responses for five second long
    maximal voluny contractions of the knee extensor are
    analyzed to understand torque response characteristics of
    di fferent subjects. The parameters identified in the data
    analysis are used in the implementation of the 'Shark and
    School of Fish' game. The behavior of the game for
    different torque responses is analyzed on a different
    torque data set from the previous study. The
    evaluationshows that the game rewards and motivates
    continuously over a repetition to reach the peak torque
    value. The evaluation also shows that the game rewards the
    user more if he overcomes a baseline torque value within
    the first second and then gradually increase the torque to
    reach peak torque.},
    address  = {Grantham-Allee 20, 53757 St. Augustin, Germany},
    annote  = {[Semester you sted the MAS Program] [Institute of
    Aerospace Medicine,German Aerospace Center] - [A Computer
    Game based Motivation System for Human Muscle Strength
    Testing] [Herpers], [Rittweger] supervising},
    author  = {Tintu Mathew},
    month = {},
    school  = {Bonn-Rhein-Sieg University of Applied Sciences},
    title = {A Computer Game based Motivation System for Human Muscle
    Strength Testing},
    year = {2012}
    }

  • S. Sharma, “Unified Approach to Motion Planning by Coordinating Mobility and Manipulability for the KUKA youBot,” Master Thesis, Grantham-Allee 20, 53757 St. Augustin, Germany, 2012.
    [BibTeX] [Abstract]

    Recent trend in robotics is the shift from using fixed base manipulators to mobile manipulators. Mobile manipulators have become of high interest to industry and service robotics sector because of the increased flexibility and effectiveness they offer due to added mobility. However, the combination of the mobility provided by a mobile platform and the manipulation capabilities provided by a robot arm leads to complex analytical problems for research. These problems can be studied very well on the KUKA youBot, a mobile manipulator designed for education and research applications. This thesis aims to achieve seamless integration and synchronization of mobility and manipulation capabilities for performing mobile manipulation tasks with the KUKA youBot. To do this, we propose a novel approach to perform unified motion planning for the KUKA youBot based on Inverse Kinematics Bi-directional Rapidly Exploring Random Trees (IKBiRRT) algorithm using Workspace Goal Regions. A closed form inverse kinematics solution for the unified kinematics of the KUKA youBot is used to implement the planner and resolve redundancies. A motion planning framework is developed that is capable of updating the elements in the world model in an online manner. Experiments are performed both in simulation and on the robot. To test the coordination between arm and base motions, the mobile manipulator plans a collision free path so that the it goes under a table or bar to simulate a limbo movement. A new approach is described to represent the workspace capabilities of the youBot into a map. This map containing the useful workspace of the mobile manipulator is termed as feasibility map. The full six dimensional continuous workspace of the youBot has been mapped to a reduced subspace in two dimensions without loss of information. The feasibility map describes the reachability and redundancy due to mobility of the youBot, in its workspace. The reachability is represented in a reachability map and is constrained by joint limits and singularities whereas a redundancy map describes the redundancy range at a particular point in the workspace. Applications of the feasibility map in motion planning, grasp planning and online obstacle avoidance scenarios are discussed.

    @MastersThesis{ 2012sharma,
    abstract  = {Recent trend in robotics is the shift from using fixed
    base manipulators to mobile manipulators. Mobile
    manipulators have become of high interest to industry and
    service robotics sector because of the increased
    flexibility and effectiveness they offer due to added
    mobility. However, the combination of the mobility provided
    by a mobile platform and the manipulation capabilities
    provided by a robot arm leads to complex analytical
    problems for research. These problems can be studied very
    well on the KUKA youBot, a mobile manipulator designed for
    education and research applications. This thesis aims to
    achieve seamless integration and synchronization of
    mobility and manipulation capabilities for performing
    mobile manipulation tasks with the KUKA youBot. To do this,
    we propose a novel approach to perform unified motion
    planning for the KUKA youBot based on Inverse Kinematics
    Bi-directional Rapidly Exploring Random Trees (IKBiRRT)
    algorithm using Workspace Goal Regions. A closed form
    inverse kinematics solution for the unified kinematics of
    the KUKA youBot is used to implement the planner and
    resolve redundancies. A motion planning framework is
    developed that is capable of updating the elements in the
    world model in an online manner. Experiments are performed
    both in simulation and on the robot. To test the
    coordination between arm and base motions, the mobile
    manipulator plans a collision free path so that the it goes
    under a table or bar to simulate a limbo movement. A new
    approach is described to represent the workspace
    capabilities of the youBot into a map. This map containing
    the useful workspace of the mobile manipulator is termed as
    feasibility map. The full six dimensional continuous
    workspace of the youBot has been mapped to a reduced
    subspace in two dimensions without loss of information. The
    feasibility map describes the reachability and redundancy
    due to mobility of the youBot, in its workspace. The
    reachability is represented in a reachability map and is
    constrained by joint limits and singularities whereas a
    redundancy map describes the redundancy range at a
    particular point in the workspace. Applications of the
    feasibility map in motion planning, grasp planning and
    online obstacle avoidance scenarios are discussed.},
    address  = {Grantham-Allee 20, 53757 St. Augustin, Germany},
    annote  = {WS 09 HBRS, KUKA Laboratories GmbH - BRICS Kraetzschmar,
    Scheurer},
    author  = {Shashank Sharma},
    month = {September},
    school  = {Bonn-Rhein-Sieg University of Applied Sciences},
    title = {Unified Approach to Motion Planning by Coordinating
    Mobility and Manipulability for the KUKA youBot},
    year = {2012}
    }

  • M. Thosar, “A Naive Approach For Learning The Semantics Of Effects Of An Action,” Master Thesis, Grantham-Allee 20, 53757 St. Augustin, Germany, 2012.
    [BibTeX] [Abstract]

    Classical artificial intelligence emphasizes that cognitive abilities such as inferring conclusions from the sensor information acquired over time, devising suitable plans, reasoning about the environment etc. are grounded in a mental representation of the world and the resulting intelligent behavior is an outcome of the correct reasoning using these representations. One of the use of these abilities is to reason about effects of an action where an agent reasons how the environment changes after executing an action. In this work, we have proposed an approach to learn the effects of an action. The effects of an action can be learned either by simply stating what changes in the environment given the current state and action description or by stating how these changes are related to the action. The latter part forms our approach. We have developed a system called EVOLT (EVOLving Theories) which for a given set of state descriptions (before and after an action is executed) and a set of action parameters of the action, represented in a predefined format, learns a theory consisting of a formal description of effects of an action in stages such that a theory relates the common changes in the state to the action parameter/s according to the given mathematical/logical operator definitions, expressed in a logic language which is referred as a semantic of the action in this setting. The main contribution of this work is twofold: one, we have proposed an extended Inductive Logic Programming(ILP) technique in a way that the extended ILP does not rely on labeled examples unlike the conventional ILP. Moreover, it is provided with a background knowledge which is general in nature and is not limited to a single action-effect theory learning task, but can be reused for a variety of learning action-effect theory tasks. Second, the representation formalism proposed in this task. The formalism offers a uniform representation for the input data in a sense that the input data is distributed in one or more groups according to some property such that the property wraps systematically the data according to a representation rule. Due to this uniformity, programmer do not have to worry about what and how much data is enlisted in each group while programming. Also, using this representation formalism, variety of inductive learning problems can be aimed. The experimental evaluation is done to examine the applicability and behavior of EVOLT in different environmental settings. The other aim was to gain insights during the experimentation which can further be used to improve the system. As the system is in its early developmental stages, we have discussed the strengths and weaknesses of the system realized during the development and evaluation. The results of the evaluation were quiet reasonable which has formed the basis for the future work we have discussed at the end of this report.

    @MastersThesis{ 2012thosar,
    abstract  = {Classical artificial intelligence emphasizes that
    cognitive abilities such as inferring conclusions from the
    sensor information acquired over time, devising suitable
    plans, reasoning about the environment etc. are grounded in
    a mental representation of the world and the resulting
    intelligent behavior is an outcome of the correct reasoning
    using these representations. One of the use of these
    abilities is to reason about effects of an action where an
    agent reasons how the environment changes after executing
    an action. In this work, we have proposed an approach to
    learn the effects of an action. The effects of an action
    can be learned either by simply stating what changes in the
    environment given the current state and action description
    or by stating how these changes are related to the action.
    The latter part forms our approach. We have developed a
    system called EVOLT (EVOLving Theories) which for a given
    set of state descriptions (before and after an action is
    executed) and a set of action parameters of the action,
    represented in a predefined format, learns a theory
    consisting of a formal description of effects of an action
    in stages such that a theory relates the common changes in
    the state to the action parameter/s according to the given
    mathematical/logical operator definitions, expressed in a
    logic language which is referred as a semantic of the
    action in this setting. The main contribution of this work
    is twofold: one, we have proposed an extended Inductive
    Logic Programming(ILP) technique in a way that the extended
    ILP does not rely on labeled examples unlike the
    conventional ILP. Moreover, it is provided with a
    background knowledge which is general in nature and is not
    limited to a single action-effect theory learning task, but
    can be reused for a variety of learning action-effect
    theory tasks. Second, the representation formalism proposed
    in this task. The formalism offers a uniform representation
    for the input data in a sense that the input data is
    distributed in one or more groups according to some
    property such that the property wraps systematically the
    data according to a representation rule. Due to this
    uniformity, programmer do not have to worry about what and
    how much data is enlisted in each group while programming.
    Also, using this representation formalism, variety of
    inductive learning problems can be aimed. The experimental
    evaluation is done to examine the applicability and
    behavior of EVOLT in different environmental settings. The
    other aim was to gain insights during the experimentation
    which can further be used to improve the system. As the
    system is in its early developmental stages, we have
    discussed the strengths and weaknesses of the system
    realized during the development and evaluation. The results
    of the evaluation were quiet reasonable which has formed
    the basis for the future work we have discussed at the end
    of this report.},
    address  = {Grantham-Allee 20, 53757 St. Augustin, Germany},
    annote  = {[WS08/09] [Kahl], [Mueller], [Ploeger] supervising},
    author  = {Madhura Thosar},
    month = {November},
    school  = {Bonn-Rhein-Sieg University of Applied Sciences},
    title = {A Naive Approach For Learning The Semantics Of Effects Of
    An Action},
    year = {2012}
    }

  • M. Füller, “Multi-step motion planning of climbing robots in 3D environments under kinodynamic constraints,” Master Thesis, Grantham-Allee 20, 53757 St. Augustin, Germany, 2012.
    [BibTeX] [Abstract]

    New locomotion techniques for service robots aim to overcome the difficulties with obstacle avoidance and dynamic settings in household environments by changing the robot’s working plane to the wall and ceiling. Besides the question of the mechanical implementation of these kind of robots, the motion planning of the robot requires another view and special attention. A spider-type robot is assumed in this thesis that is able to move along given footholds in the environment. The motion planning task is to determine the required steps to move to the goal along available docking points at the walls and the ceiling. A previous study of the author developed a multi-step motion planner for such kind of robot. The planner is able to plan multi-step motions while being aware of kinematic constraints and object collisions. One aspect that was identified during that work but not implemented is the awareness of dynamic constraints of such a robot. The motion of a legged robot – especially of climbing ones – requires special attention to the dynamic limitations. These dynamic limitations are for example maximum possible joint torques during the climbing motions. This work is the next step toward a more realistic multi-step motion planner for 3D indoor environments along walls and ceilings. It extends the existing kinematic multi-step motion planner presented by Füller (2011) with additional awareness of dynamic constraints. The planner is further integrated into a real-world physic simulation environment. The final multi-step planner can be used to evaluate required components for a real implementation of such kind of robot. It can identify problems in the planning process due to limits on the robot design and the used components before and during the development of such a robot. Additionally, the thesis presents a possible approach for an extension of a pure kinematic sample-based motion planner toward dynamic awareness.

    @MastersThesis{ 2012fueller,
    author  = {Matthias F{\"u}ller},
    title = {Multi-step motion planning of climbing robots in 3D
    environments under kinodynamic constraints},
    school  = {Bonn-Rhein-Sieg University of Applied Sciences},
    year = {2012},
    address  = {Grantham-Allee 20, 53757 St. Augustin, Germany},
    month = {March},
    abstract  = {New locomotion techniques for service robots aim to
    overcome the difficulties with obstacle avoidance and
    dynamic settings in household environments by changing the
    robot's working plane to the wall and ceiling. Besides the
    question of the mechanical implementation of these kind of
    robots, the motion planning of the robot requires another
    view and special attention. A spider-type robot is assumed
    in this thesis that is able to move along given footholds
    in the environment. The motion planning task is to
    determine the required steps to move to the goal along
    available docking points at the walls and the ceiling.
    A previous study of the author developed a multi-step
    motion planner for such kind of robot. The planner is able
    to plan multi-step motions while being aware of kinematic
    constraints and object collisions. One aspect that was
    identified during that work but not implemented is the
    awareness of dynamic constraints of such a robot. The
    motion of a legged robot - especially of climbing ones -
    requires special attention to the dynamic limitations.
    These dynamic limitations are for example maximum possible
    joint torques during the climbing motions. This work is the
    next step toward a more realistic multi-step motion planner
    for 3D indoor environments along walls and ceilings. It
    extends the existing kinematic multi-step motion planner
    presented by F{\"u}ller (2011) with additional awareness of
    dynamic constraints. The planner is further integrated into
    a real-world physic simulation environment. The final
    multi-step planner can be used to evaluate required
    components for a real implementation of such kind of robot.
    It can identify problems in the planning process due to
    limits on the robot design and the used components before
    and during the development of such a robot. Additionally,
    the thesis presents a possible approach for an extension of
    a pure kinematic sample-based motion planner toward dynamic
    awareness.},
    annote  = {W09, Prassler, Forsman supervising}
    }

  • R. Dwiputra, “Dynamic Modeling of KUKA youBot Manipulator,” Master Thesis, Grantham-Allee 20, 53757 St. Augustin, Germany, 2012.
    [BibTeX] [Abstract]

    Models and simulation tools are crucial in robotic research. Experimentation with models is cost and time efficient due to its flexibility to be automated, conditioned, and accelerated. In this master thesis, a model of the KUKA youBot manipulator was developed with Modelica. Modelica is a multi-domain modeling language description which is capable of bridging mechanical, electrical, hydraulic, and thermodynamic domain in a single model. With Modelica, the model developed incorporated dynamic parameters, motor specifications, and control system. The advantages of a robot model only holds true when its accuracy has been validated with the real robot. Before further research involving the model is executed, it should be ensured that the model behaviours reflect the actual system behaviours. Therefore, experiments have been performed in parallel with the model development to assure the model’s accuracy. The experiments performed in this master thesis are: 1. Validation of the robot controller model; 2. Friction model approximation; and 3. Validation of the robot model for different motions and modes. The results show that the model reflects the actual system behaviour to a certain extent. The experiment results and the model can be further used for experiment involving control system, load identification, and trajectory generation algorithm.

    @MastersThesis{ 2012dwiputra,
    author  = {Rhama Dwiputra},
    title = {Dynamic Modeling of KUKA youBot Manipulator},
    year = {2012},
    month = {October},
    abstract  = {Models and simulation tools are crucial in robotic
    research. Experimentation with models is cost and time
    efficient due to its flexibility to be automated,
    conditioned, and accelerated. In this master thesis, a
    model of the KUKA youBot manipulator was developed with
    Modelica. Modelica is a multi-domain modeling language
    description which is capable of bridging mechanical,
    electrical, hydraulic, and thermodynamic domain in a single
    model. With Modelica, the model developed incorporated
    dynamic parameters, motor specifications, and control
    system.
    The advantages of a robot model only holds true when its
    accuracy has been validated with the real robot. Before
    further research involving the model is executed, it should
    be ensured that the model behaviours reflect the actual
    system behaviours. Therefore, experiments have been
    performed in parallel with the model development to assure
    the model's accuracy. The experiments performed in this
    master thesis are: 1. Validation of the robot controller
    model; 2. Friction model approximation; and 3. Validation
    of the robot model for different motions and modes. The
    results show that the model reflects the actual system
    behaviour to a certain extent. The experiment results and
    the model can be further used for experiment involving
    control system, load identification, and trajectory
    generation algorithm.},
    address  = {Grantham-Allee 20, 53757 St. Augustin, Germany},
    annote  = {[Semester you started the MAS Program] [Project
    Affiliation] - [Project Name] [Last name of 1st
    Supervisor], [Last name of 2nd supervisor], [last name of
    third supervisor (if applicable)] supervising},
    school  = {Bonn-Rhein-Sieg University of Applied Sciences}
    }

  • M. Arasi, “Particle Filter Based Approach for the Diagnosis of Unknown External Faults in a Mobile Manipulator,” Master Thesis, Grantham-Allee 20, 53757 St. Augustin, Germany, 2012.
    [BibTeX] [Abstract]

    Monitoring complex systems (e.g. Mobile Manipulator) for the sake of estimation and fault diagnosis, is a topic of considerable dominance in scientific literature. Autonomous systems often stop performing their tasks because of occurrences of unexpected faults. Here, unexpected or unknown fault is defined as the fault that occurs in the system’s dynamic environment and cannot be observed by the system’s sensors because of their limitations. Fault diagnosis is a prerequisite for robust operation of a system in a hazardous or changing environment. Fault diagnosis comprises of different procedures represented by fault detection, isolation and fault identification. In recent years, many researchers have investigated diagnosis problems, and many of them proposed particle filter (PF) as a solution. Particle filter is a Bayesian approach used in many applications such as fault diagnosis to approximate the belief distribution of the state of the system as soon as the observation is available. It has successfully implemented on a complete system model that is running under internal faults. In the recent works, for the systems dealing with unknown faults, researchers assume that the approach (i.e. Particle Filter) is working on un-modeled or imperfectly modeled system. This assumption leads only to the detection of the unknown faults but cannot identify the main cause of the failure. However, this information is not enough, especially for the recovery procedure. Since it is important and useful for the fault diagnosis approach to incorporate the disturbances and unexpected faults that occur in a system, this is the main issue handled in the thesis. In brief, developing an efficient fault diagnosis approach for the unknown external faults is suggested to be the solution for the aforementioned problems. In addition, robustness, correctness and efficiency of the proposed approach has been investigated, in diagnosing the state of the system. Our fault diagnosis approach is based on Particle Filter (PF). As an evidence of working of the approach, we use a set of test case scenarios for a simulated mobile manipulator, in openRAVE [22]. On the same set of test case scenarios, we applied the Classical Particle Filter (CPF) and the Gaussian Particle Filter (GPF). The approach is applied on a hybrid dynamic system in the application of reliable navigation for a mobile robot and efficient object transfer from the initial to the target position for a robot manipulator. In our approach, we have assumed that during the occurrences of unknown fault the system’s hardware and software keeps functioning perfectly. Extensive simulations have been carried out to test the performance of the approach under different number of particles. The experimental results show the effectiveness of the approach on the given set of use case scenarios. The results show that both the approaches, CPF and GPF, are equally good at diagnosing unknown external faults but the GPF functions a lot better for the diagnosis of the unknown external faults even under less number of particles. The proposed diagnosis approach is able not only to diagnose the fault, but also to estimate some valuable informations needed for the recovery process, such as collision position for a navigated mobile robot.

    @MastersThesis{ 2012arasi,
    abstract  = {Monitoring complex systems (e.g. Mobile Manipulator) for
    the sake of estimation and fault diagnosis, is a topic of
    considerable dominance in scientific literature. Autonomous
    systems often stop performing their tasks because of
    occurrences of unexpected faults. Here, unexpected or
    unknown fault is defined as the fault that occurs in the
    system's dynamic environment and cannot be observed by the
    system's sensors because of their limitations. Fault
    diagnosis is a prerequisite for robust operation of a
    system in a hazardous or changing environment. Fault
    diagnosis comprises of different procedures represented by
    fault detection, isolation and fault identification. In
    recent years, many researchers have investigated diagnosis
    problems, and many of them proposed particle filter (PF) as
    a solution. Particle filter is a Bayesian approach used in
    many applications such as fault diagnosis to approximate
    the belief distribution of the state of the system as soon
    as the observation is available. It has successfully
    implemented on a complete system model that is running
    under internal faults. In the recent works, for the systems
    dealing with unknown faults, researchers assume that the
    approach (i.e. Particle Filter) is working on un-modeled or
    imperfectly modeled system. This assumption leads only to
    the detection of the unknown faults but cannot identify the
    main cause of the failure. However, this information is not
    enough, especially for the recovery procedure. Since it is
    important and useful for the fault diagnosis approach to
    incorporate the disturbances and unexpected faults that
    occur in a system, this is the main issue handled in the
    thesis. In brief, developing an efficient fault diagnosis
    approach for the unknown external faults is suggested to be
    the solution for the aforementioned problems. In addition,
    robustness, correctness and efficiency of the proposed
    approach has been investigated, in diagnosing the state of
    the system. Our fault diagnosis approach is based on
    Particle Filter (PF). As an evidence of working of the
    approach, we use a set of test case scenarios for a
    simulated mobile manipulator, in openRAVE [22]. On the same
    set of test case scenarios, we applied the Classical
    Particle Filter (CPF) and the Gaussian Particle Filter
    (GPF). The approach is applied on a hybrid dynamic system
    in the application of reliable navigation for a mobile
    robot and efficient object transfer from the initial to the
    target position for a robot manipulator. In our approach,
    we have assumed that during the occurrences of unknown
    fault the system's hardware and software keeps functioning
    perfectly. Extensive simulations have been carried out to
    test the performance of the approach under different number
    of particles. The experimental results show the
    effectiveness of the approach on the given set of use case
    scenarios. The results show that both the approaches, CPF
    and GPF, are equally good at diagnosing unknown external
    faults but the GPF functions a lot better for the diagnosis
    of the unknown external faults even under less number of
    particles. The proposed diagnosis approach is able not only
    to diagnose the fault, but also to estimate some valuable
    informations needed for the recovery process, such as
    collision position for a navigated mobile robot.},
    address  = {Grantham-Allee 20, 53757 St. Augustin, Germany},
    annote  = {WS08/09 FH-BRS - Particle Filter Based Approach for the
    Diagnosis of Unknown External Faults in a Mobile
    Manipulator Pl{\"o}ger, M{\"u}ller, K{\"u}stenmacher
    supervising},
    author  = {Musherah Arasi},
    month = {October},
    school  = {Bonn-Rhein-Sieg University of Applied Sciences},
    title = {Particle Filter Based Approach for the Diagnosis of
    Unknown External Faults in a Mobile Manipulator},
    year = {2012}
    }

  • P. Banerjee, “Design and Implementation of a Software Development Toolkit to provide Perception Functionalities for Robots,” Master Thesis, Grantham-Allee 20, 53757 St. Augustin, Germany, 2012.
    [BibTeX] [Abstract]

    Robotics is a vast, interdisciplinary field of study that demands expertise of multiple disciplines like com- puter science, mathematics, electronics, mechanics, etc. Leaving the hardware part aside, even robotic software development involves integration of multiple processing components focused on different chal- lenges like, control, manipulation, navigation, perception, planning, etc. In recent development scenarios, a group of researchers usually focus on different sub-domains of expertise individually and then the sub- domain applications are integrated into complete robotic application software. To ease the software development process and reduce requirement of comprehensive knowledge of multiple sub-domains, Software Development Toolkits (SDKs) can be used. The SDKs will provide a set of high-level Application Programming Interfaces (APIs) for different robotic-domains. The APIs can be used to trigger predefined processing steps. To use the APIs only working knowledge of the corre- sponding functionalities will be required. This will enable development of a complete robotic application with comprehensive knowledge of certain sub-domains and abstract working information for the rest. For example, a planning domain expert can implement the software logics for task planning and simply reuse the APIs from perception and manipulation domain to trigger the perception and manipulation processes as governed by the implemented planning module. However, in the current state of the art of open-source robotic softwares, Software Development Toolkits for robotic manipulation, control and perception are very limited. This work aims to develop a SDK in the field of 3D robotic perception that should be able to provide dedicated interfaces to trigger high level perception functionalities like ”object detection”, ”6D pose estimation” and so on. The interface will be designed considering two classes of users for the SDK, domain-users 1 and domain-experts 2 . Based on available domain knowledge the developers can use high-level APIs to parametrize and trigger a processing step or low-level APIs to re-configure the software module and setup the algorithms and parameters to be used for processing.

    @MastersThesis{ 2012banerjee,
    abstract  = {Robotics is a vast, interdisciplinary field of study that
    demands expertise of multiple disciplines like com- puter
    science, mathematics, electronics, mechanics, etc. Leaving
    the hardware part aside, even robotic software development
    involves integration of multiple processing components
    focused on different chal- lenges like, control,
    manipulation, navigation, perception, planning, etc. In
    recent development scenarios, a group of researchers
    usually focus on different sub-domains of expertise
    individually and then the sub- domain applications are
    integrated into complete robotic application software. To
    ease the software development process and reduce
    requirement of comprehensive knowledge of multiple
    sub-domains, Software Development Toolkits (SDKs) can be
    used. The SDKs will provide a set of high-level Application
    Programming Interfaces (APIs) for different
    robotic-domains. The APIs can be used to trigger predefined
    processing steps. To use the APIs only working knowledge of
    the corre- sponding functionalities will be required. This
    will enable development of a complete robotic application
    with comprehensive knowledge of certain sub-domains and
    abstract working information for the rest. For example, a
    planning domain expert can implement the software logics
    for task planning and simply reuse the APIs from perception
    and manipulation domain to trigger the perception and
    manipulation processes as governed by the implemented
    planning module. However, in the current state of the art
    of open-source robotic softwares, Software Development
    Toolkits for robotic manipulation, control and perception
    are very limited. This work aims to develop a SDK in the
    field of 3D robotic perception that should be able to
    provide dedicated interfaces to trigger high level
    perception functionalities like ”object detection”,
    ”6D pose estimation” and so on. The interface will be
    designed considering two classes of users for the SDK,
    domain-users 1 and domain-experts 2 . Based on available
    domain knowledge the developers can use high-level APIs to
    parametrize and trigger a processing step or low-level APIs
    to re-configure the software module and setup the
    algorithms and parameters to be used for processing. },
    address  = {Grantham-Allee 20, 53757 St. Augustin, Germany},
    annote  = {WS-09,GPS EU-BRICS, Prassler, Blumenthal, Zakharov},
    author  = {Pinaki Banerjee},
    month = {February},
    year = {2012},
    school  = {Bonn-Rhein-Sieg University of Applied Sciences},
    title = {Design and Implementation of a Software Development
    Toolkit to provide Perception Functionalities for Robots}
    }

  • E. Dayangac, “Vision-based 6DoF Input Device Development with Ground Truth Evaluation,” Master Thesis, Grantham-Allee 20, 53757 St. Augustin, Germany, 2012.
    [BibTeX] [Abstract]

    Different visual tracking approaches exists, among them feature-based and template- based are prominent, for determining position and orientation of an object or camera. Visual tracking has been used for several applications ranging from visual odometry, augmented reality, 3D object modeling, medical imaging, visual simultaneous localization and mapping to surveillance. The work presented in this thesis designs a visual tracking system to recover the full 6 degree-of-freedom pose of a camera with developing an evaluation methodology for performance analysis of the system. The work presented in this thesis includes design of a visual tracking system to recover the full 6 degree-of-freedom pose of a camera and also development of an evaluation methodology to analyze the performance of the tracking system. Marker-based tracking and visualization system is implemented for CAVE-type virtual reality environments. The system consists of a wired CCD camera as the only sensor and multiple unique markers. The markers are placed into a video picture and projected within a scene using a projector Except the calibration of projector and camera, the system does not need additional calibration between the scene and the tracking system. The system is affordable comparing to other existing tracking systems. The drawbacks of the system are the high power consumption and cabling requirement due to the CCD camera and the visibility of the markers within the scene. However, rapid development in imaging hardware makes the system affordable, and useful in the near future. The LCD projectors are used, hence the visualized marker within the scene is in the visible range of human eye under the current implementation. The system still needs a solution of a light module at invisible wavelength. Camera pose estimation is very common in the field of Computer Vision, which makes the implementation of a tracking system a trivial task mentioned above except; performance analysis is a challenging task. The extensive evaluation of a tracking algorithm is still an open issue in research, even though there are some attempts to address the problem. In this thesis, we categorized evaluation methods on visual tracking systems. We can conclude that the existing approaches solves the problem partially, and insufficient to discover weaknesses and strengths of an algorithm. Knowledge from the experiments does not clearly show the failures of the algorithms to improve the results, because their main purpose was to compare some particular approaches. Also, an evaluation methodology has been proposed and a framework is implemented. The framework includes the camera and visualization system additionally with a 5DoF computer-controlled robotic arm and an optical tracking system. The arm is used to move the camera systematically, while optical tracking system is used to get a rough idea about the pose of the robot-base. A new system and distributed software architectures are presented in the report. This framework is capable of collecting static image sequences repeatedly around a 3D point-list. Featuring different texture images can be collected in a short time. During run-time, it does on-line image processing with stitching reference data. The data analysis part is done manually afterwards, which is one of the incapabilities of the system. Due to the deficient robotic arm, the motion pattern is limited and collected images are static. Consequently, the implemented marker based tracking system and the optical tracking system are evaluated partially.

    @MastersThesis{ 2012dayangac,
    abstract  = {Different visual tracking approaches exists, among them
    feature-based and template- based are prominent, for
    determining position and orientation of an object or
    camera. Visual tracking has been used for several
    applications ranging from visual odometry, augmented
    reality, 3D object modeling, medical imaging, visual
    simultaneous localization and mapping to surveillance.
    The work presented in this thesis designs a visual tracking
    system to recover the full 6 degree-of-freedom pose of a
    camera with developing an evaluation methodology for
    performance analysis of the system. The work presented in
    this thesis includes design of a visual tracking system to
    recover the full 6 degree-of-freedom pose of a camera and
    also development of an evaluation methodology to analyze
    the performance of the tracking system.
    Marker-based tracking and visualization system is
    implemented for CAVE-type virtual reality environments. The
    system consists of a wired CCD camera as the only sensor
    and multiple unique markers. The markers are placed into a
    video picture and projected within a scene using a projector
    Except the calibration of projector and camera, the system
    does not need additional calibration between the scene and
    the tracking system. The system is affordable comparing to
    other existing tracking systems. The drawbacks of the
    system are the high power consumption and cabling
    requirement due to the CCD camera and the visibility of the
    markers within the scene. However, rapid development in
    imaging hardware makes the system affordable, and useful in
    the near future. The LCD projectors are used, hence the
    visualized marker within the scene is in the visible range
    of human eye under the current implementation. The system
    still needs a solution of a light module at invisible
    wavelength.
    Camera pose estimation is very common in the field of
    Computer Vision, which makes the implementation of a
    tracking system a trivial task mentioned above except;
    performance analysis is a challenging task.
    The extensive evaluation of a tracking algorithm is still
    an open issue in research, even though there are some
    attempts to address the problem. In this thesis, we
    categorized evaluation methods on visual tracking systems.
    We can conclude that the existing approaches solves the
    problem partially, and insufficient to discover weaknesses
    and strengths of an algorithm. Knowledge from the
    experiments does not clearly show the failures of the
    algorithms to improve the results, because their main
    purpose was to compare some particular approaches.
    Also, an evaluation methodology has been proposed and a
    framework is implemented. The framework includes the camera
    and visualization system additionally with a 5DoF
    computer-controlled robotic arm and an optical tracking
    system. The arm is used to move the camera systematically,
    while optical tracking system is used to get a rough idea
    about the pose of the robot-base. A new system and
    distributed software architectures are presented in the
    report.
    This framework is capable of collecting static image
    sequences repeatedly around a 3D point-list. Featuring
    different texture images can be collected in a short time.
    During run-time, it does on-line image processing with
    stitching reference data. The data analysis part is done
    manually afterwards, which is one of the incapabilities of
    the system. Due to the deficient robotic arm, the motion
    pattern is limited and collected images are static.
    Consequently, the implemented marker based tracking system
    and the optical tracking system are evaluated partially.},
    address  = {Grantham-Allee 20, 53757 St. Augustin, Germany},
    annote  = {SS08/09,Herpers, Hinkenjann, Saitov supervising},
    author  = {Enes Dayangac},
    month = {April},
    school  = {Bonn-Rhein-Sieg University of Applied Sciences},
    title = {Vision-based 6DoF Input Device Development with Ground
    Truth Evaluation},
    year = {2012}
    }

  • N. Akhtar, “Improving reliability of mobile manipulators against unknown external faults,” Master Thesis, Grantham-Allee 20, 53757 St. Augustin, Germany, 2012.
    [BibTeX] [Abstract]

    A robot (e.g. mobile manipulator) that interacts with its environment to perform its tasks, often faces situations in which it is unable to achieve its goals despite perfect functioning of its sensors and actuators. These situations occur when the behavior of the objects manipulated by the robot deviates from its expected course because of unforeseeable circumstances. These deviations are experienced by the robot as unknown external faults which result in failures of its actions. In this work we present an approach that increases the reliability of mobile manipulators against the unknown external faults. This approach focuses on the actions of manipulators which involve releasing of an object. The approach presented in this work is formulated as a three step scheme that takes an example simulation and a definition of a planning operator as its inputs. The example simulation is a simulation that shows the expected/desired behavior of the object upon the execution of the action corresponded by the planning operator. In its first step, the scheme finds a description of the behavior of the objects in the example simulation in terms of logical atoms. We refer to these logical atoms collectively as description vocabulary. The description of the simulation is used by the second step to find limits of the parameters of the manipulated object. Using randomly chosen values of the parameters within these limits, this step creates different examples of the releasing state of the object. These examples are labelled as desired or undesired according to the behavior of the object in the simulation. The description vocabulary is also used in labeling the examples. In the third step, an algorithm (i.e. N-Bins) uses the labelled examples to suggest the state for the object in which releasing it avoids the occurrence of any unknown external faults. The proposed N-Bins algorithm can also be used for binary classification problem. Therefore, in our experiments with the proposed approach we also test its prediction ability along with the analysis of the results of our approach. The results show that under the circumstances peculiar to our approach, N-Bins algorithm show reasonable prediction accuracy where other state of the art classification algorithms fail to do so. Thus, N-Bins also extends the ability of a robot to predict the behavior of the object to avoid unknown external faults. In this work we use simulation environment OPENRave that uses physics engine ODE to simulate the dynamics of rigid bodies.

    @MastersThesis{ 2012akhtar,
    address  = {Grantham-Allee 20, 53757 St. Augustin, Germany},
    author  = {Naveed Akhtar},
    date-modified  = {2012-02-18 17:07:45 +0100},
    keywords  = {external faults, mobile manipulators, binary
    classification},
    month = {February},
    school  = {Bonn-Rhein-Sieg University of Applied Sciences},
    title = {Improving reliability of mobile manipulators against
    unknown external faults},
    year = {2012},
    abstract  = {A robot (e.g. mobile manipulator) that interacts with its
    environment to perform its tasks, often faces situations in
    which it is unable to achieve its goals despite perfect
    functioning of its sensors and actuators. These situations
    occur when the behavior of the objects manipulated by the
    robot deviates from its expected course because of
    unforeseeable circumstances. These deviations are
    experienced by the robot as unknown external faults which
    result in failures of its actions. In this work we present
    an approach that increases the reliability of mobile
    manipulators against the unknown external faults. This
    approach focuses on the actions of manipulators which
    involve releasing of an object. The approach presented in
    this work is formulated as a three step scheme that takes
    an example simulation and a definition of a planning
    operator as its inputs. The example simulation is a
    simulation that shows the expected/desired behavior of the
    object upon the execution of the action corresponded by the
    planning operator. In its first step, the scheme finds a
    description of the behavior of the objects in the example
    simulation in terms of logical atoms. We refer to these
    logical atoms collectively as description vocabulary. The
    description of the simulation is used by the second step to
    find limits of the parameters of the manipulated object.
    Using randomly chosen values of the parameters within these
    limits, this step creates different examples of the
    releasing state of the object. These examples are labelled
    as desired or undesired according to the behavior of the
    object in the simulation. The description vocabulary is
    also used in labeling the examples. In the third step, an
    algorithm (i.e. N-Bins) uses the labelled examples to
    suggest the state for the object in which releasing it
    avoids the occurrence of any unknown external faults.
    The proposed N-Bins algorithm can also be used for binary
    classification problem. Therefore, in our experiments with
    the proposed approach we also test its prediction ability
    along with the analysis of the results of our approach. The
    results show that under the circumstances peculiar to our
    approach, N-Bins algorithm show reasonable prediction
    accuracy where other state of the art classification
    algorithms fail to do so. Thus, N-Bins also extends the
    ability of a robot to predict the behavior of the object to
    avoid unknown external faults. In this work we use
    simulation environment OPENRave that uses physics engine
    ODE to simulate the dynamics of rigid bodies.
    },
    annote  = {WS 09/10, H-BRS, Pl{\"o}ger, Asteroth, Kuestenmacher
    supervising }
    }

2011

  • C. A. Mueller, “3D Object Shape Categorization in Domestic Environments,” Master Thesis, Grantham-Allee 20, 53757 St. Augustin, Germany, 2011.
    [BibTeX] [Abstract]

    In service robotics, tasks without the involvement of objects are barely applicable, like in searching, fetching or delivering tasks. Service robots are supposed to capture efficiently object related information in real world scenes while for instance considering clutter and noise, and also being flexible and scalable to memorize a large set of objects. Besides object perception tasks like object recognition where the object’s identity is analyzed, object categorization is an important visual object perception cue that associates unknown object instances based on their e.g. appearance or shape to a corresponding category. We present a pipeline from the detection of object candidates in a domestic scene over the description to the final shape categorization of detected candidates. In order to detect object related information in cluttered domestic environments an object detection method is proposed that copes with multiple plane and object occurrences like in cluttered scenes with shelves. Further a surface reconstruction method based on Growing Neural Gas (GNG) in combination with a shape distribution-based descriptor is proposed to reflect shape characteristics of object candidates. Beneficial properties provided by the GNG such as smoothing and denoising effects support a stable description of the object candidates which also leads towards a more stable learning of categories. Based on the presented descriptor a dictionary approach combined with a supervised shape learner is presented to learn prediction models of shape categories. Experimental results, of different shapes related to domestically appearing object shape categories such as cup, can, box, bottle, bowl, plate and ball, are shown. A classifica- tion accuracy of about 90% and a sequential execution time of lesser than two seconds for the categorization of an unknown object is achieved which proves the reasonableness of the proposed system design. Additional results are shown towards object tracking and false positive handling to enhance the robustness of the categorization. Also an initial approach towards incremental shape category learning is proposed that learns a new category based on the set of previously learned shape categories.

    @MastersThesis{ 2011mueller,
    author  = {Christian Atanas Mueller},
    title = {3D Object Shape Categorization in Domestic Environments},
    school  = {Bonn-Rhine-Sieg University of Applied Sciences},
    year = {2011},
    address  = {Grantham-Allee 20, 53757 St. Augustin, Germany},
    month = {December},
    abstract  = {In service robotics, tasks without the involvement of
    objects are barely applicable, like in searching, fetching
    or delivering tasks. Service robots are supposed to capture
    efficiently object related information in real world scenes
    while for instance considering clutter and noise, and also
    being flexible and scalable to memorize a large set of
    objects. Besides object perception tasks like object
    recognition where the object's identity is analyzed, object
    categorization is an important visual object perception cue
    that associates unknown object instances based on their
    e.g. appearance or shape to a corresponding category. We
    present a pipeline from the detection of object candidates
    in a domestic scene over the description to the final shape
    categorization of detected candidates. In order to detect
    object related information in cluttered domestic
    environments an object detection method is proposed that
    copes with multiple plane and object occurrences like in
    cluttered scenes with shelves. Further a surface
    reconstruction method based on Growing Neural Gas (GNG) in
    combination with a shape distribution-based descriptor is
    proposed to reflect shape characteristics of object
    candidates. Beneficial properties provided by the GNG such
    as smoothing and denoising effects support a stable
    description of the object candidates which also leads
    towards a more stable learning of categories. Based on the
    presented descriptor a dictionary approach combined with a
    supervised shape learner is presented to learn prediction
    models of shape categories. Experimental results, of
    different shapes related to domestically appearing object
    shape categories such as cup, can, box, bottle, bowl, plate
    and ball, are shown. A classifica- tion accuracy of about
    90% and a sequential execution time of lesser than two
    seconds for the categorization of an unknown object is
    achieved which proves the reasonableness of the proposed
    system design. Additional results are shown towards object
    tracking and false positive handling to enhance the
    robustness of the categorization. Also an initial approach
    towards incremental shape category learning is proposed
    that learns a new category based on the set of previously
    learned shape categories.},
    annote  = {[Winter term 2008][BRSU] - [RoboCup@Home][Ploeger],
    [Kraetzschmar], [Hochgeschwender] supervising}
    }

  • M. Shahzad, “Detection and tracking of pointing hand gestures for AR applications,” Master Thesis, Grantham-Allee 20, 53757 St. Augustin, Germany, 2011.
    [BibTeX] [Abstract]

    In the field of augmented reality (AR) user interfaces and service robotics, directly using users hand provide natural way to interact with the computers. Apart from other gestures, pointing gestures are one of the most commonly used gestures in everyday life. They can enable natural and convenient way for humans to interact with computers. In this thesis, a real time approach for detecting such natural pointing hand gestures for user interaction is presented. It uses data provided by a stereoscopic camera system capable of giving dense disparity maps and an estimation of 3D point cloud information. The developed approach allows the user to conveniently interact using natural pointing hand gestures with different components/modules of the biological laboratory Biolab. The Biolab is an International Standard Payload Rack (ISPR) of European Space Agency (ESA) and is operated by German Aerospace Center (Deutsches Zentrum fr Luft- und Raumfahrt e.V. – DLR) during space missions. It is an integral part of the European science laboratory, Columbus, which is part of the International Space Station (ISS). The approach uses skin color and 3D data to extract probable hand regions in an image. These regions are further refined to detect the pointing hand contour using minimum distance approach by projecting the 3D contour data onto virtual plane representing the Biolab. Feature points (hand center and fingertip) are found in the detected contour to estimate the pointing direction and a 3D virtual beam is fitted between them. The fingertip projection is used to switch between two semantically modeled Biolab representations in two different levels. The virtual plane and estimated 3D beam is used in the identification of pointing targets in both the levels. The developed approach is able to work in real time without any markers or other sensors attached to the pointing hand. It is person independent and has the potential to cope with different skin colors and complex background and does not require any manual initialization procedure. Moreover, it does not put any constrain on the user to wear special clothing and detects pointing hand and identifies the pointed targets even in cases if the user is not wearing full sleeves shirt. The algorithm is thoroughly evaluated with 8 different persons under two different lighting conditions in sitting and standing postures. The pointing targets are correctly identified as can be verified by inspection for natural pointing hand gestures performed by different test subjects. Identification results presented later in the thesis illustrates the effectiveness of the approach.

    @MastersThesis{ 2011shahzad,
    abstract  = {In the field of augmented reality (AR) user interfaces and
    service robotics, directly using users hand provide
    natural way to interact with the computers. Apart from
    other gestures, pointing gestures are one of the most
    commonly used gestures in everyday life. They can enable
    natural and convenient way for humans to interact with
    computers. In this thesis, a real time approach for
    detecting such natural pointing hand gestures for user
    interaction is presented. It uses data provided by a
    stereoscopic camera system capable of giving dense
    disparity maps and an estimation of 3D point cloud
    information. The developed approach allows the user to
    conveniently interact using natural pointing hand gestures
    with different components/modules of the biological
    laboratory Biolab. The Biolab is an International Standard
    Payload Rack (ISPR) of European Space Agency (ESA) and is
    operated by German Aerospace Center (Deutsches Zentrum fr
    Luft- und Raumfahrt e.V. - DLR) during space missions. It
    is an integral part of the European science laboratory,
    Columbus, which is part of the International Space Station
    (ISS). The approach uses skin color and 3D data to extract
    probable hand regions in an image. These regions are
    further refined to detect the pointing hand contour using
    minimum distance approach by projecting the 3D contour data
    onto virtual plane representing the Biolab. Feature points
    (hand center and fingertip) are found in the detected
    contour to estimate the pointing direction and a 3D virtual
    beam is fitted between them. The fingertip projection is
    used to switch between two semantically modeled Biolab
    representations in two different levels. The virtual plane
    and estimated 3D beam is used in the identification of
    pointing targets in both the levels. The developed approach
    is able to work in real time without any markers or other
    sensors attached to the pointing hand. It is person
    independent and has the potential to cope with different
    skin colors and complex background and does not require any
    manual initialization procedure. Moreover, it does not put
    any constrain on the user to wear special clothing and
    detects pointing hand and identifies the pointed targets
    even in cases if the user is not wearing full sleeves
    shirt. The algorithm is thoroughly evaluated with 8
    different persons under two different lighting conditions
    in sitting and standing postures. The pointing targets are
    correctly identified as can be verified by inspection for
    natural pointing hand gestures performed by different test
    subjects. Identification results presented later in the
    thesis illustrates the effectiveness of the approach. },
    address  = {Grantham-Allee 20, 53757 St. Augustin, Germany},
    annote  = {ws08 DLR - Detection and tracking of pointing hand
    gestures for AR applications Herpers, Plger, Mittag},
    author  = {Muhammad Shahzad},
    month = {March},
    school  = {Bonn-Rhein-Sieg University of Applied Sciences},
    title = {Detection and tracking of pointing hand gestures for AR
    applications},
    year = {2011}
    }

  • M. Hoffmann, “A simulation environment for distributed stream analysis,” Master Thesis, Grantham-Allee 20, 53757 St. Augustin, Germany, 2011.
    [BibTeX] [Abstract]

    This work is part of the European research project LIFT – Using Local Inference in Massively Distributed Systems. The goal of LIFT is to apply local inference on nodes of a massively distributed system in order to analyze the systems global phenomena in near real-time. Nowadays distributed systems i.e. large data centers or huge distributed networks grow larger and larger. Out of these circumstances, the problem of monitoring and analyzing these systems becomes more and more complex and almost unmanageable. The goal of this work is to develop a simulation framework for the LIFT context. This framework will support and ease the implementation, testing and evaluation of different data stream filters and global phenomena- (or state-) determination algorithms. This simulation environment intends to make the platform independent prototyping process for distributed stream analysis manageable. The adaptation of an existing multi agent simulation environment will be shown, together with techniques on how to overcome limitation to enable simulation of massively distributed systems. Additionally a framework will be developed which will support the rapid prototyping of local filters. The adapted simulation environment will be evaluated by discussing its computational and parameter dependant scalability. Additionally a filter model, the privacy preserving spatial filter model will be introduced and applied to a real world scenario for validation.

    @MastersThesis{ 2011hoffmannmarius,
    author  = {Marius Hoffmann},
    title = {A simulation environment for distributed stream analysis},
    school  = {Bonn-Rhein-Sieg University of Applied Science},
    year = {2011},
    address  = {Grantham-Allee 20, 53757 St. Augustin, Germany},
    month = {September},
    annote  = {[SS09][LIFT] - [Using Local Inference in Massively
    Distributed Systems][PD Dr. Michael Mock (IAIS)][Prof. Dr.
    Paul G. Pl\"oger (H-BRS)]},
    abstract  = {This work is part of the European research project LIFT --
    Using Local Inference in Massively Distributed Systems. The
    goal of LIFT is to apply local inference on nodes of a
    massively distributed system in order to analyze the
    systems global phenomena in near real-time. Nowadays
    distributed systems i.e. large data centers or huge
    distributed networks grow larger and larger. Out of these
    circumstances, the problem of monitoring and analyzing
    these systems becomes more and more complex and almost
    unmanageable. The goal of this work is to develop a
    simulation framework for the LIFT context. This framework
    will support and ease the implementation, testing and
    evaluation of different data stream filters and global
    phenomena- (or state-) determination algorithms. This
    simulation environment intends to make the platform
    independent prototyping process for distributed stream
    analysis manageable. The adaptation of an existing multi
    agent simulation environment will be shown, together with
    techniques on how to overcome limitation to enable
    simulation of massively distributed systems. Additionally a
    framework will be developed which will support the rapid
    prototyping of local filters. The adapted simulation
    environment will be evaluated by discussing its
    computational and parameter dependant scalability.
    Additionally a filter model, the privacy preserving spatial
    filter model will be introduced and applied to a real world
    scenario for validation.}
    }

  • F. Hegger, “3D People Detection in Domestic Environments,” Master Thesis, Grantham-Allee 20, 53757 St. Augustin, Germany, 2011.
    [BibTeX] [Abstract]

    The ability of detecting people has become a crucial subtask, especially in robotic systems which aim an application in public or domestic environments. Robots already provide their services e.g. in real home improvement markets and guide people to a desired product. In such a scenario many robot internal tasks would benefit from the knowledge of knowing the number and positions of people in the vicinity. The navigation for example could treat them as dynamical moving objects and also predict their next motion directions in order to compute a much safer path. Or the robot could specifically approach customers and offer its services. This requires to detect a person or even a group of people in a reasonable range in front of the robot. Challenges of such a real-world task are e.g. changing lightning conditions, a dynamic environment and different people shapes. In this thesis a 3D people detection approach based on point cloud data provided by the Microsoft Kinect is implemented and integrated on mobile service robot. A Top-Down/Bottom-Up segmentation is applied to increase the systems flexibility and provided the capability to the detect people even if they are partially occluded. A feature set is proposed to detect people in various pose configurations and motions using a machine learning technique. The system can detect people up to a distance of 5 meters. The experimental evaluation compared different machine learning techniques and showed that standing people can be detected with a rate of 87.29% and sitting people with 74.94% using a Random Forest classifier. Certain objects caused several false detections. To elimante those a verification is proposed which further evaluates the persons shape in the 2D space. The detection component has been implemented as s sequential (frame rate of 10 Hz) and a parallel application (frame rate of 16 Hz). Finally, the component has been embedded into complete people search task which explorates the environment, find all people and approach each detected person.

    @MastersThesis{ 2011hegger,
    abstract  = {The ability of detecting people has become a crucial
    subtask, especially in robotic systems which aim an
    application in public or domestic environments. Robots
    already provide their services e.g. in real home
    improvement markets and guide people to a desired product.
    In such a scenario many robot internal tasks would benefit
    from the knowledge of knowing the number and positions of
    people in the vicinity. The navigation for example could
    treat them as dynamical moving objects and also predict
    their next motion directions in order to compute a much
    safer path. Or the robot could specifically approach
    customers and offer its services. This requires to detect a
    person or even a group of people in a reasonable range in
    front of the robot. Challenges of such a real-world task
    are e.g. changing lightning conditions, a dynamic
    environment and different people shapes. In this thesis a
    3D people detection approach based on point cloud data
    provided by the Microsoft Kinect is implemented and
    integrated on mobile service robot. A Top-Down/Bottom-Up
    segmentation is applied to increase the systems flexibility
    and provided the capability to the detect people even if
    they are partially occluded. A feature set is proposed to
    detect people in various pose configurations and motions
    using a machine learning technique. The system can detect
    people up to a distance of 5 meters. The experimental
    evaluation compared different machine learning techniques
    and showed that standing people can be detected with a rate
    of 87.29% and sitting people with 74.94% using a Random
    Forest classifier. Certain objects caused several false
    detections. To elimante those a verification is proposed
    which further evaluates the persons shape in the 2D space.
    The detection component has been implemented as s
    sequential (frame rate of 10 Hz) and a parallel application
    (frame rate of 16 Hz). Finally, the component has been
    embedded into complete people search task which explorates
    the environment, find all people and approach each detected
    person. },
    address  = {Grantham-Allee 20, 53757 St. Augustin, Germany},
    annote  = {[Winter term 2008] [BRSU] - [RoboCup@Home] [Ploeger],
    [Kraetzschmar], [Hochgeschwender] supervising},
    author  = {Frederik Hegger},
    month = {October},
    school  = {Bonn-Rhine-Sieg University of Applied Sciences},
    title = {3D People Detection in Domestic Environments},
    year = {2011}
    }

  • Q. Fu, “The CUDA Acceleration of SIFT algorithm on GPU,” Master Thesis, Grantham Allee 20, 53757 St. Augustin, Germany, 2011.
    [BibTeX] [Abstract]

    Since the SIFT algorithm was invented in 1999, it has been well applied in several fields such as object recognition, robotic mapping and navigation, image stitching, 3D model- ing, gesture recognition, video tracking, and match moving. Because of the steadiness and distinctiveness of SIFT, it has become the most popular research topic or tool in computer vision. However, in order to apply SIFT in real time, speed is the biggest difficulty. Since in the SIFT algorithm, the original image has to be modified several times, such as convolution and calculate the Difference of Gaussian. Thus, if the image size is big, the workload would grow exponentially. It would take seconds to perform SIFT on an image with resolution 1024*768. In this thesis, a GPU is used to accelerate SIFT. By taking the advantage of parallel computing and proper management of memory on graphic card, the algorithm is accelerate about 4 times and the result shows that the size of image would not influence the time cost as big as in the CPU implementation.

    @MastersThesis{ 2011fu,
    abstract  = {Since the SIFT algorithm was invented in 1999, it has been
    well applied in several fields such as object recognition,
    robotic mapping and navigation, image stitching, 3D model-
    ing, gesture recognition, video tracking, and match moving.
    Because of the steadiness and distinctiveness of SIFT, it
    has become the most popular research topic or tool in
    computer vision. However, in order to apply SIFT in real
    time, speed is the biggest difficulty. Since in the SIFT
    algorithm, the original image has to be modified several
    times, such as convolution and calculate the Difference of
    Gaussian. Thus, if the image size is big, the workload
    would grow exponentially. It would take seconds to perform
    SIFT on an image with resolution 1024*768. In this thesis,
    a GPU is used to accelerate SIFT. By taking the advantage
    of parallel computing and proper management of memory on
    graphic card, the algorithm is accelerate about 4 times and
    the result shows that the size of image would not influence
    the time cost as big as in the CPU implementation. },
    address  = {Grantham Allee 20, 53757 St. Augustin, Germany},
    annote  = {WS 2006/07, Kraetschmar, Pl\"{o}ger supervising},
    author  = {Quiang Fu},
    month = {February},
    school  = {Bonn-Rhein-Sieg University of Applied Sciences},
    title = {The CUDA Acceleration of SIFT algorithm on GPU },
    year = {2011}
    }

  • G. Giorgana, “Facial Expression Recognition from Video Sequences using Spatial and Spatiotemporal Face Descriptors,” Master Thesis, Grantham Allee 20, 53757 St. Augustin, Germany, 2011.
    [BibTeX] [Abstract]

    In the last decades, more natural and efficient channels of communication between humans and robots have been investigated. Most of the efforts have been toward analysis of spoken language, but the existing speech-based solutions are still highly error-prone. As a result, researchers have started to consider human facial expressions as an important form to improve the spoken communication. In this thesis we describe three effective methods for the extraction of features that can be used for the recognition of human facial expressions: 2D Gabor filters, Local Binary Patterns (LBP) and Local Binary Patterns from Three Orthogonal Planes (LBP-TOP). Moreover, we investigate the effect of using different sets of parameters on the three approaches. Depending on the technique, the recognition of the expressions is done in still images or complete video sequences. The Cohn-Kanade AU-Coded Facial Expression Database is used throughout this work. All the experiments in this report are carried out using the AdaBoost.MH algorithm. We describe how this ensemble learner can cope with our multi-class problem, while also reducing the number of features. Furthermore, the system is evaluated for two different kinds of weak learners, namely multi-threshold decision stumps and single-threshold decision stumps. Another question we address in this project is the effect of histogram equalization for the face images. Therefore, the impact of this technique in the three methods is analyzed. From the three explored approaches, LBP-TOP takes into account the facial motion that occurs due to facial expressions. On the other hand, 2D Gabor Filters and LBP only describe the instantaneous face appearance, ignoring temporal information. Taking the previous information into account, we compare the approaches and show the advantages of considering motion cues into the analysis. Finally, we present experimental evidence that the three techniques are suitable to recognize facial expressions from live video.

    @MastersThesis{ 2011giorgana,
    author  = {Geovanny Giorgana},
    title = {Facial Expression Recognition from Video Sequences using
    Spatial and Spatiotemporal Face Descriptors},
    school  = {Bonn-Rhein-Sieg University of Applied Sciences},
    year = {2011},
    address  = {Grantham Allee 20, 53757 St. Augustin, Germany},
    month = {February},
    abstract  = {In the last decades, more natural and efficient channels
    of communication between humans and robots have been
    investigated. Most of the efforts have been toward analysis
    of spoken language, but the existing speech-based solutions
    are still highly error-prone. As a result, researchers have
    started to consider human facial expressions as an
    important form to improve the spoken communication.
    In this thesis we describe three effective methods for the
    extraction of features that can be used for the recognition
    of human facial expressions: 2D Gabor filters, Local Binary
    Patterns (LBP) and Local Binary Patterns from Three
    Orthogonal Planes (LBP-TOP). Moreover, we investigate the
    effect of using different sets of parameters on the three
    approaches. Depending on the technique, the recognition of
    the expressions is done in still images or complete video
    sequences. The Cohn-Kanade AU-Coded Facial Expression
    Database is used throughout this work.
    All the experiments in this report are carried out using
    the AdaBoost.MH algorithm. We describe how this ensemble
    learner can cope with our multi-class problem, while also
    reducing the number of features. Furthermore, the system is
    evaluated for two different kinds of weak learners, namely
    multi-threshold decision stumps and single-threshold
    decision stumps.
    Another question we address in this project is the effect
    of histogram equalization for the face images. Therefore,
    the impact of this technique in the three methods is
    analyzed.
    From the three explored approaches, LBP-TOP takes into
    account the facial motion that occurs due to facial
    expressions. On the other hand, 2D Gabor Filters and LBP
    only describe the instantaneous face appearance, ignoring
    temporal information. Taking the previous information into
    account, we compare the approaches and show the advantages
    of considering motion cues into the analysis. Finally, we
    present experimental evidence that the three techniques are
    suitable to recognize facial expressions from live video.},
    annote  = {WS07/08 H-BRS - RoboCup@home Pl\"{o}ger, Kraetzschmar
    supervising}
    }

  • M. U. Awais, “An adaptive search engine for Power Point objects,” Master Thesis, Grantham-Allee 20, 53757 St. Augustin, Germany, 2011.
    [BibTeX] [Abstract]

    In large scale organizations, with active information technology infrastructure, there is almost no concept of inception of a group discussion, without a formal presentation. In large organizations, volume of these presentations increases rapidly. In order to reuse these presentations, there is a need to develop a knowledge management application which intelligently manages this large amount of data. Intelligent searching should be one part of this application. This report presents a search methodology which is adaptive and intelligent. Using this search methodology a search engine is developed, to search Power Point objects. This engine may be used in conjunction with Microsoft Power Point and other softwares for preparing presentations. To make this search intelligent, two things are introduced. One is, latent semantic analysis, while second is, incorporating user feedback in a way that desires and expectations of users can vary the ranking of results. An innovative design is presented, which delicately inter relates both of these ideas, to present an adaptive behavior of the search system. While testing this application, a new idea for testing a search system is presented. The idea is to use artificial agents for testing, by automating user behaviors.

    @MastersThesis{ 2011awais,
    abstract  = {In large scale organizations, with active information
    technology infrastructure, there is almost no concept of
    inception of a group discussion, without a formal
    presentation. In large organizations, volume of these
    presentations increases rapidly. In order to reuse these
    presentations, there is a need to develop a knowledge
    management application which intelligently manages this
    large amount of data. Intelligent searching should be one
    part of this application. This report presents a search
    methodology which is adaptive and intelligent. Using this
    search methodology a search engine is developed, to search
    Power Point objects. This engine may be used in conjunction
    with Microsoft Power Point and other softwares for
    preparing presentations. To make this search intelligent,
    two things are introduced. One is, latent semantic
    analysis, while second is, incorporating user feedback in a
    way that desires and expectations of users can vary the
    ranking of results. An innovative design is presented,
    which delicately inter relates both of these ideas, to
    present an adaptive behavior of the search system. While
    testing this application, a new idea for testing a search
    system is presented. The idea is to use artificial agents
    for testing, by automating user behaviors.},
    address  = {Grantham-Allee 20, 53757 St. Augustin, Germany},
    annote  = {[2008] [Text mining] - [An adaptive search engine for
    Power Point objects] [Herpers], [Ploeger], [Willebrand]
    supervising},
    author  = {Muhammad Usman Awais},
    month = {March},
    school  = {Bonn-Rhein-Sieg University of Applied Sciences},
    title = {An adaptive search engine for Power Point objects},
    year = {2011}
    }

2010

  • F. K. Heinstein, “Improving the Processing and Visualization of Ultrasound Imaging on GPU with CUDA,” Master Thesis, 2010.
    [BibTeX] [Abstract]

    Phased Array Ultrasound Technologies (PAUT) techniques is one the Non-Destructive Testing (NDT) methods where we have the possibility to perform inspections with ultrasonic beams of various angles and focal lengths using a single array of transducers. There are software application that precisely controlled the delay of both the emission pulse and the receive signal for each element in an array of transducers. However the processing of the receive beam forming requires a lot of computing power during the image reconstruction phase. The tremendous growth in Graphics Processors Unit (GPU) performance and flexibility has led to an increased interest in performing general-purpose computation on GPU. GPU provides a vast number of simple, data parallel, deeply multithreaded cores and high memory bandwidths. GPU architectures are becoming increasingly programmable, offering the potential for dramatic speedups for a variety of general-purpose applications compared to contemporary general-purpose processors (CPU). That is why with this master thesis we want to explore the area of general purpose computing on GPU, by looking at how the GPU can be utilized to accelerate the processing of ultrasound image. We will use CUDA (Compute Uni ed Device Architecture) which is a language from NVIDIA close to C programming language as a programming model for this GPU implementation. CUDA is a novel technology of general-purpose computing on the GPU allowing users to develop general GPU programs easily. In this thesis I will explore the e ffectiveness of GPU in Ultrasound Image and describes some specific coding idioms that improve their performance on the GPU. GPU performance will be compared to both single-thread version executed on the single-core CPU and multi-threaded OpenMP version executed on the multi-core CPU.

    @MastersThesis{ heinstein2010,
    abstract  = {Phased Array Ultrasound Technologies (PAUT) techniques is
    one the Non-Destructive Testing (NDT) methods where we have
    the possibility to perform inspections with ultrasonic
    beams of various angles and focal lengths using a single
    array of transducers. There are software application that
    precisely controlled the delay of both the emission pulse
    and the receive signal for each element in an array of
    transducers. However the processing of the receive beam
    forming requires a lot of computing power during the image
    reconstruction phase.
    The tremendous growth in Graphics Processors Unit (GPU)
    performance and flexibility has led to an increased
    interest in performing general-purpose computation on GPU.
    GPU provides a vast number of simple, data parallel, deeply
    multithreaded cores and high memory bandwidths. GPU
    architectures are becoming increasingly programmable,
    offering the potential for dramatic speedups for a variety
    of general-purpose applications compared to contemporary
    general-purpose processors (CPU). That is why with this
    master thesis we want to explore the area of general
    purpose computing on GPU, by looking at how the GPU can be
    utilized to accelerate the processing of ultrasound image.
    We will use CUDA (Compute Uni ed Device Architecture) which
    is a language from NVIDIA close to C programming language
    as a programming model for this GPU implementation. CUDA is
    a novel technology of general-purpose computing on the GPU
    allowing users to develop general GPU programs easily.
    In this thesis I will explore the e ffectiveness of GPU in
    Ultrasound Image and describes some specific coding idioms
    that improve their performance on the GPU. GPU performance
    will be compared to both single-thread version executed on
    the single-core CPU and multi-threaded OpenMP version
    executed on the multi-core CPU.},
    author  = {Heinstein, Fotso Kamgne},
    keywords  = {Non-Destructive Testing NDT, Phased Array Testing
    Ultrasound Technologies (PAUT), Ultrasound Images, High
    Performance Computing(HPC), General-Purpose Graphics
    Processing Units (GPGPU), CUDA, OpenMP, Parallel
    Programming, Multithreading, Multicore},
    month = {July},
    owner = {108012516},
    school  = {Hochschule Bonn-Rhein-Sieg},
    timestamp  = {2010.09.08},
    title = {Improving the Processing and Visualization of Ultrasound
    Imaging on GPU with CUDA},
    year = {2010}
    }

  • U. Köckemann, “A Relational Robotics Workbench for Learning Experiments with Autonomous Robots,” Master Thesis, 2010.
    [BibTeX] [Abstract]

    Even though relational learning approaches, as Inductive Logic Programming and Statistical Relational Learning have great potential benefits in the area of robotic learning, there only have been few applications. We belief one of the reasons to be the large effort involved with performing relational learning experiments in real robotic domains. To resolve these issues and facilite research in this direction we propose the \textit{Relational Robotics Workbench}, a tool that aids researchers in conducting Inductive logic Programming and Statistical Relational Learning experiments in robotic domains. By maximizing re-usability of all components, as learning and inference components on the relational level, or algorithms used for robot control, the \textit{Relational Robotics Workbench} significantly reduces the amount of work in creating relational learning experiments. Furthermore, the learning experiments themselves are made explicit, allowing to store the robots setup, execution of plans (to gather training and test data), setup and execution of learning algorithms and evaluation of the final hypotheses in an easy-to-read configuration file. Functionality provided by the \textit{Workbench} includes: Inductive Logic Programming with Aleph, Statistical Relational Learning With Alchemy, Logic Programming Inference with Prolog, and Statistical Relational Inference with Alchemy. We will provide an analysis of requirements of the proposed \textit{Workbench} and derive a four layered architecture to fulfill them. Along with the description of the architecture, we will provide a full learning experiment from the XPERO project as a running example. To further demonstrate usefulness of the \textit{Workbench}, we then will perform another series of experiments in the ego-motion scenario of the XPERO project. The experiments include descriptions of some additional algorithms implemented in the Workbench, as for instance, a simple scheme to automatically extract learning examples to apply supervised learning methods in an unsupervised fashion. Results for all learning experiments will be presented and examples of the theories that were learned in each scenario will be discussed.

    @MastersThesis{ koeckemann10relational,
    author  = {K{\"o}ckemann, Uwe},
    title = {A Relational Robotics Workbench for Learning Experiments
    with Autonomous Robots},
    school  = {Bonn-Rhein-Sieg University of Applied Sciences},
    year = {2010},
    abstract  = {Even though relational learning approaches, as Inductive
    Logic Programming and Statistical Relational Learning have
    great potential benefits in the area of robotic learning,
    there only have been few applications. We belief one of the
    reasons to be the large effort involved with performing
    relational learning experiments in real robotic domains. To
    resolve these issues and facilite research in this
    direction we propose the \textit{Relational Robotics
    Workbench}, a tool that aids researchers in conducting
    Inductive logic Programming and Statistical Relational
    Learning experiments in robotic domains.
    By maximizing re-usability of all components, as learning
    and inference components on the relational level, or
    algorithms used for robot control, the \textit{Relational
    Robotics Workbench} significantly reduces the amount of
    work in creating relational learning experiments.
    Furthermore, the learning experiments themselves are made
    explicit, allowing to store the robots setup, execution of
    plans (to gather training and test data), setup and
    execution of learning algorithms and evaluation of the
    final hypotheses in an easy-to-read configuration file.
    Functionality provided by the \textit{Workbench} includes:
    Inductive Logic Programming with Aleph, Statistical
    Relational Learning With Alchemy, Logic Programming
    Inference with Prolog, and Statistical Relational Inference
    with Alchemy.
    We will provide an analysis of requirements of the proposed
    \textit{Workbench} and derive a four layered architecture
    to fulfill them. Along with the description of the
    architecture, we will provide a full learning experiment
    from the XPERO project as a running example. To further
    demonstrate usefulness of the \textit{Workbench}, we then
    will perform another series of experiments in the
    ego-motion scenario of the XPERO project. The experiments
    include descriptions of some additional algorithms
    implemented in the Workbench, as for instance, a simple
    scheme to automatically extract learning examples to apply
    supervised learning methods in an unsupervised fashion.
    Results for all learning experiments will be presented and
    examples of the theories that were learned in each scenario
    will be discussed. }
    }

  • N. Khayat, “Monitoring and Analysis of Workflows in Learning Environments,” Master Thesis, Grantham Allee 20, 53757 St. Augustin, Germany, 2010.
    [BibTeX] [Abstract]

    With the prosperous spread of new technologies, Technology-Enabled Learning (TEL) Environments are playing a more signicant role, as a mean of delivering an intelligent learning process to the learners. In the context of the European SCY project, a collaborative, learners-centric TEL environment is being developed. Using the SCY system, the learners would execute a mission, represented as a workow, which could be considered as a provided plan for the learners to follow. In this thesis, we have developed a monitoring and analysis system, to be integrated with the SCY system. With the developed system, reference workows and workow executions will be analysed to extract meaningful patterns. The extracted patterns will be used to provide the teachers and pedagogical experts with knowledge insights about the missions executions. Meaningful behavioral attributes are extracted automatically from those patterns, which expresses the behavior model of the learner.

    @MastersThesis{ 2010khayat,
    abstract  = {With the prosperous spread of new technologies,
    Technology-Enabled Learning (TEL) Environments are playing
    a more signicant role, as a mean of delivering an
    intelligent learning process to the learners.
    In the context of the European SCY project, a
    collaborative, learners-centric TEL environment is being
    developed. Using the SCY system, the learners would execute
    a mission, represented as a workow, which could be
    considered as a provided plan for the learners to follow.
    In this thesis, we have developed a monitoring and analysis
    system, to be integrated with the SCY system. With the
    developed system, reference workows and workow executions
    will be analysed to extract meaningful patterns. The
    extracted patterns will be used to provide the teachers and
    pedagogical experts with knowledge insights about the
    missions executions. Meaningful behavioral attributes are
    extracted automatically from those patterns, which
    expresses the behavior model of the learner.},
    address  = {Grantham Allee 20, 53757 St. Augustin, Germany},
    annote  = {WS07/08 Fraunhofer IAIS - SCY "Science Created by You"
    Mock, Pl{\"o}ger supervising},
    author  = {Noury Khayat},
    month = {May},
    school  = {Bonn-Rhein-Sieg University of Applied Sciences},
    title = {Monitoring and Analysis of Workflows in Learning
    Environments},
    year = {2010}
    }

  • N. Kharecha, “Robotic Perception for Object Grasping: A ToF Camera based Approach for Environment Sensing,” Master Thesis, Sankt Augustin, Germany, 2010.
    [BibTeX] [Abstract]

    If 3D model of the grasping body is available, object surface can be evaluated for given gripper using force-closure criteria. In real-life house hold scenario, such models are not available. To determine grasp for any object, skill to interpret object geometry is required. As grasping of the object occur in 3D world, range sensors are considered better choice of perception. Different ranging devices provide range data but are either time consuming to capture (row-by-row laser scanning) or sparse (stereo camera). Such requirements of skill of grasp determination of model-free objects considering limitation of different ranging device, task of the thesis aim to develop Time-of-flight camera based robotic perception system to determine grasp for unknown object. Time-of-flight camera provides full scale range information at video frame rate but depth measurements are affected by different parameters. Different distance correction schemes are evaluated to suppress noise level. To interpret viewing scene, segmentation of table-top scenario is done to hypothesize different object segments. Single part grasping body is considered to be placed on top of the surface; elongated representation of the footprint of the object segment is presented. As an earlier stage of implementation, a very primitive grasp pose is computed for given elongated representation of the object segment.

    @MastersThesis{ 2010kharecha,
    author  = {Kharecha, Nimeshkumar},
    title = {Robotic Perception for Object Grasping: A ToF Camera based
    Approach for Environment Sensing},
    school  = {Bonn-Rhine-Sieg University of Applied Sciences},
    year = {2010},
    address  = {Sankt Augustin, Germany},
    month = {May},
    abstract  = {If 3D model of the grasping body is available, object
    surface can be evaluated for given gripper using
    force-closure criteria. In real-life house hold scenario,
    such models are not available. To determine grasp for any
    object, skill to interpret object geometry is required. As
    grasping of the object occur in 3D world, range sensors are
    considered better choice of perception. Different ranging
    devices provide range data but are either time consuming to
    capture (row-by-row laser scanning) or sparse (stereo
    camera).
    Such requirements of skill of grasp determination of
    model-free objects considering limitation of different
    ranging device, task of the thesis aim to develop
    Time-of-flight camera based robotic perception system to
    determine grasp for unknown object. Time-of-flight camera
    provides full scale range information at video frame rate
    but depth measurements are affected by different
    parameters. Different distance correction schemes are
    evaluated to suppress noise level. To interpret viewing
    scene, segmentation of table-top scenario is done to
    hypothesize different object segments. Single part grasping
    body is considered to be placed on top of the surface;
    elongated representation of the footprint of the object
    segment is presented. As an earlier stage of
    implementation, a very primitive grasp pose is computed for
    given elongated representation of the object segment.},
    keywords  = {ToF camera, computational geometry, grasp determination}
    }

  • U. Kayani, “ESNs with sparse output connections,” Master Thesis, Grantham Allee 20, 53757 St. Augustin, Germany, 2010.
    [BibTeX] [Abstract]

    This is your abstract.

    @MastersThesis{ 2010kayani,
    abstract  = {This is your abstract.},
    address  = {Grantham Allee 20, 53757 St. Augustin, Germany},
    annote  = {[Summer Semester 2006] [Project Affiliation] - [DEGENA]
    [Dr. Kobialka] and [Prof. Dr. Ploeger]},
    author  = {Umer Kayani},
    date-added  = {2016-09-04 11:54:23 +0000},
    date-modified  = {2016-09-04 11:54:23 +0000},
    month = {January},
    school  = {Bonn-Rhein-Sieg University of Applied Sciences},
    title = {ESNs with sparse output connections},
    year = {2010}
    }

  • J. Kang, “Interactive Medical Image Segmentation via User-guided Adaptation of Implicit 3D Surfaces,” Master Thesis, Grantham Allee 20, 53757 St. Augustin, Germany, 2010.
    [BibTeX] [Abstract]

    Medical image segmentation plays a very important role in medical image analysis and is considered as a very challenging problem. In this work, a new interactive medical image segmentation framework via user-guided adaptation of implicit 3D surfaces is proposed. The surfaces are mathematically described by a radial basis function that interpolates some user-defined points. This framework allows two kinds of user interactions: Firstly, via mouse clicks the user can define points on the boundary of objects in 3D images. For each point, a 3D surface which interpolates the user-determined points is computed. The user can add or remove points with instant visual feedback until the desired accuracy is reached. Secondly, the user can start an automatic adaption. In this step, the surface computed in step one automatically evolves by maximizing a certain energy function with only external energy. Strong image edges are interpolated without losing the interpolation property of the user-defined points. Our framework is validated by segmenting lung tumors on CT data. Some good segmentation results by this framework show its potential for practical usage.

    @MastersThesis{ 2010kang,
    abstract  = {Medical image segmentation plays a very important role in
    medical image analysis and is considered as a very
    challenging problem. In this work, a new interactive
    medical image segmentation framework via user-guided
    adaptation of implicit 3D surfaces is proposed. The
    surfaces are mathematically described by a radial basis
    function that interpolates some user-defined points. This
    framework allows two kinds of user interactions: Firstly,
    via mouse clicks the user can define points on the boundary
    of objects in 3D images. For each point, a 3D surface which
    interpolates the user-determined points is computed. The
    user can add or remove points with instant visual feedback
    until the desired accuracy is reached. Secondly, the user
    can start an automatic adaption. In this step, the surface
    computed in step one automatically evolves by maximizing a
    certain energy function with only external energy. Strong
    image edges are interpolated without losing the
    interpolation property of the user-defined points. Our
    framework is validated by segmenting lung tumors on CT
    data. Some good segmentation results by this framework show
    its potential for practical usage.},
    address  = {Grantham Allee 20, 53757 St. Augustin, Germany},
    annote  = {WS06/07 Philips Hamburg Research Center Opfer,
    Kraetzschmar},
    author  = {Jingang Kang},
    month = {June},
    school  = {Bonn-Rhein-Sieg University of Applied Sciences},
    title = {Interactive Medical Image Segmentation via User-guided
    Adaptation of Implicit 3D Surfaces},
    year = {2010}
    }

2008

  • A. Zakharov, “Robust navigation in everyday environment,” Master Thesis, Grantham Allee 20, 53757 St. Augustin, Germany, 2008.
    [BibTeX] [Abstract]

    Autonomous navigation is one of the challenging task in robotics. In order to navigate safely, a robot needs robust and reliable sensing mechanisms. Though there are a variety of range sensors for obstacle detection on the market, there is no straight forward solution, which sensor’s system and data processing mechanism apply in order to get reliable and low cost robot navigation for everyday environment. There is no ultimate range sensor, each type of sensors has it own advantages and disadvantages. The goal of this project is to design a navigation system, which is a combination of affordable range sensors (hardware) and data processing and enhancement algorithms (software), which would eliminate the sensors’ shortcomings.

    @MastersThesis{ 2008zakharov,
    abstract  = {Autonomous navigation is one of the challenging task in
    robotics. In order to navigate safely, a robot needs robust
    and reliable sensing mechanisms. Though there are a variety
    of range sensors for obstacle detection on the market,
    there is no straight forward solution, which sensor's
    system and data processing mechanism apply in order to get
    reliable and low cost robot navigation for everyday
    environment. There is no ultimate range sensor, each type
    of sensors has it own advantages and disadvantages. The
    goal of this project is to design a navigation system,
    which is a combination of affordable range sensors
    (hardware) and data processing and enhancement algorithms
    (software), which would eliminate the sensors'
    shortcomings.},
    address  = {Grantham Allee 20, 53757 St. Augustin, Germany},
    annote  = {SS06 [Prassler], [Shakhimardanov]},
    author  = {Alexey Zakharov},
    month = {June},
    school  = {Bonn-Rhein-Sieg University of Applied Sciences},
    title = {Robust navigation in everyday environment},
    year = {2008}
    }