Dr. Frank Hesse

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    Year
    Title
    Journal / Proceedings / Book
    Kesper, P. and Grinke, E. and Hesse, F. and Wörgötter, F. and Manoonpong, P. (2013).
    Obstacle/Gap Detection and Terrain Classification of Walking Robots based on a 2D Laser Range Finder. 16th International Conference on Climbing and Walking Robots and the Support Technologies for Mobile Machines CLAWAR, 419-426, 16.
    BibTeX:
    @inproceedings{kespergrinkehesse2013,
      author = {Kesper, P. and Grinke, E. and Hesse, F. and Wörgötter, F. and Manoonpong, P.},
      title = {Obstacle/Gap Detection and Terrain Classification of Walking Robots based on a 2D Laser Range Finder},
      pages = {419-426},
      booktitle = {16th International Conference on Climbing and Walking Robots and the Support Technologies for Mobile Machines CLAWAR},
      year = {2013},
      number = {16},
      location = {Sidney (Australia)},
      month = {July 14-17},
      abstract = {This paper utilizes a 2D laser range finder (LRF) to determine the behavior of a walking robot. The LRF provides information for 1) obstacle/gap detection as well as 2) terrain classification. The obstacle/gap detection is based on an edge detection with increased robustness and accuracy due to customized pre and post processing. Its output is used to drive obstacle/gap avoidance behavior or climbing behavior, depending on the height of obstacles or the depth of gaps. The terrain classification employs terrain roughness to select a proper gait with respect to the current terrain. As a result, the combination of these methods enables the robot to decide if obstacles and gaps can be climbed up/down or have to be avoided while at the same time a terrain specific gait can be chosen.}}
    Abstract: This paper utilizes a 2D laser range finder (LRF) to determine the behavior of a walking robot. The LRF provides information for 1) obstacle/gap detection as well as 2) terrain classification. The obstacle/gap detection is based on an edge detection with increased robustness and accuracy due to customized pre and post processing. Its output is used to drive obstacle/gap avoidance behavior or climbing behavior, depending on the height of obstacles or the depth of gaps. The terrain classification employs terrain roughness to select a proper gait with respect to the current terrain. As a result, the combination of these methods enables the robot to decide if obstacles and gaps can be climbed up/down or have to be avoided while at the same time a terrain specific gait can be chosen.
    Review:
    Hesse, F. and Wörgötter, F. (2013).
    A goal-orientation framework for self-organizing control. Advances in Complex Systems, 1350002, 16, 02n03. DOI: 10.1142/S0219525913500021.
    BibTeX:
    @article{hessewoergoetter2013,
      author = {Hesse, F. and Wörgötter, F.},
      title = {A goal-orientation framework for self-organizing control},
      pages = {1350002},
      journal = {Advances in Complex Systems},
      year = {2013},
      volume= {16},
      number = {02n03},
      url = {http://www.worldscientific.com/doi/abs/10.1142/S0219525913500021},
      doi = {10.1142/S0219525913500021},
      abstract = {Self-organization, especially in the framework of embodiment in biologically inspired robots, allows the acquisition of behavioral primitives by autonomous robots themselves. However, it is an open question how self-organization of basic motor primitives and goal-orientation can be combined, which is a prerequisite for the usefulness of such systems. In the paper at hand we propose a goal-orientation framework allowing the combination of self-organization and goal-orientation for the control of autonomous robots in a mutually independent fashion. Self-organization based motor primitives are employed to achieve a given goal. This requires less initial knowledge about the properties of robot and environment and increases adaptivity of the overall system. A combination of self-organization and reward-based learning seems thus a promising route for the development of adaptive learning systems.}}
    Abstract: Self-organization, especially in the framework of embodiment in biologically inspired robots, allows the acquisition of behavioral primitives by autonomous robots themselves. However, it is an open question how self-organization of basic motor primitives and goal-orientation can be combined, which is a prerequisite for the usefulness of such systems. In the paper at hand we propose a goal-orientation framework allowing the combination of self-organization and goal-orientation for the control of autonomous robots in a mutually independent fashion. Self-organization based motor primitives are employed to achieve a given goal. This requires less initial knowledge about the properties of robot and environment and increases adaptivity of the overall system. A combination of self-organization and reward-based learning seems thus a promising route for the development of adaptive learning systems.
    Review:
    Goldschmidt, D. and Hesse, F. and Wörgötter, F. and Manoonpong, P. (2012).
    Biologically inspired reactive climbing behavior of hexapod robots. IEEE/RSJ International Conference on Intelligent Robots and Systems IROS, 4632-4637. DOI: 10.1109/IROS.2012.6386135.
    BibTeX:
    @inproceedings{goldschmidthessewoergoetter2012,
      author = {Goldschmidt, D. and Hesse, F. and Wörgötter, F. and Manoonpong, P.},
      title = {Biologically inspired reactive climbing behavior of hexapod robots},
      pages = {4632-4637},
      booktitle = {IEEE/RSJ International Conference on Intelligent Robots and Systems IROS},
      year = {2012},
      doi = {10.1109/IROS.2012.6386135},
      abstract = {Insects, e.g. cockroaches and stick insects, have found fascinating solutions for the problem of locomotion, especially climbing over a large variety of obstacles. Research on behavioral neurobiology has identified key behavioral patterns of these animals (i.e., body flexion, center of mass elevation, and local leg reflexes) necessary for climbing. Inspired by this finding, we develop a neural control mechanism for hexapod robots which generates basic walking behavior and especially enables them to effectively perform reactive climbing behavior. The mechanism is composed of three main neural circuits: locomotion control, reactive backbone joint control, and local leg reflex control. It was developed and tested using a physical simulation environment, and was then successfully transferred to a physical six-legged walking machine, called AMOS II. Experimental results show that the controller allows the robot to overcome obstacles of various heights (e.g., 75% of its leg length, which are higher than those that other comparable legged robots have achieved so far). The generated climbing behavior is also comparable to the one observed in cockroaches.}}
    Abstract: Insects, e.g. cockroaches and stick insects, have found fascinating solutions for the problem of locomotion, especially climbing over a large variety of obstacles. Research on behavioral neurobiology has identified key behavioral patterns of these animals (i.e., body flexion, center of mass elevation, and local leg reflexes) necessary for climbing. Inspired by this finding, we develop a neural control mechanism for hexapod robots which generates basic walking behavior and especially enables them to effectively perform reactive climbing behavior. The mechanism is composed of three main neural circuits: locomotion control, reactive backbone joint control, and local leg reflex control. It was developed and tested using a physical simulation environment, and was then successfully transferred to a physical six-legged walking machine, called AMOS II. Experimental results show that the controller allows the robot to overcome obstacles of various heights (e.g., 75% of its leg length, which are higher than those that other comparable legged robots have achieved so far). The generated climbing behavior is also comparable to the one observed in cockroaches.
    Review:
    Hesse, F. and Manoonpong, P. and Wörgötter, F. (2011).
    A Neural Pre- and Post-Processing Framework for Goal Directed Behavior in Self-Organizing Robots. The 21st Annual Conference of the Japanese Neural Network Society.
    BibTeX:
    @inproceedings{hessemanoonpongwoergoetter2011,
      author = {Hesse, F. and Manoonpong, P. and Wörgötter, F.},
      title = {A Neural Pre- and Post-Processing Framework for Goal Directed Behavior in Self-Organizing Robots},
      booktitle = {The 21st Annual Conference of the Japanese Neural Network Society},
      year = {2011},
      abstract = {In the work at hand we introduce a neural pre- and post-processing framework whose parameters can be adapted by any learning mechanism, e.g. reinforcement learning. The framework allows to generate goal-directed behaviors while at the same time exploiting the bene cial properties, e.g. robustness, of self-organization based primitive behaviors}}
    Abstract: In the work at hand we introduce a neural pre- and post-processing framework whose parameters can be adapted by any learning mechanism, e.g. reinforcement learning. The framework allows to generate goal-directed behaviors while at the same time exploiting the bene cial properties, e.g. robustness, of self-organization based primitive behaviors
    Review:

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