Patrick Kesper

Group(s): Neural Control and Robotics
Email:
pkesper@gwdg.de

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    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.
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