Robotics: Science and Systems V

Non-parametric learning to aid path planning over slopes

S. Karumanchi, T. Allen, T. Bailey and S. Scheding

Abstract:

This paper addresses the problem of closing the loop from perception to action selection for unmanned ground vehicles, with a focus on navigating slopes. A new non-parametric learning technique is presented to generate a mobility representation where maximum feasible speed is used as a criterion to classify the world. The inputs to the algorithm are terrain gradients derived from an elevation map and past observations of wheel slip. It is argued that such a representation can aid in path planning with improved selection of vehicle heading and operating velocity in off-road slopes. Results of mobility map generation and its benefits to path planning are shown.

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Bibtex:

@INPROCEEDINGS{ Karumanchi-RSS-09,
    AUTHOR    = {S. Karumanchi AND T. Allen AND T. Bailey AND S. Scheding},
    TITLE     = {Non-parametric learning to aid path planning over slopes},
    BOOKTITLE = {Proceedings of Robotics: Science and Systems},
    YEAR      = {2009},
    ADDRESS   = {Seattle, USA},
    MONTH     = {June},
    DOI       = {10.15607/RSS.2009.V.028} 
}