Robotics: Science and Systems V

Non-parametric learning to aid path planning over slopes

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


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.



@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}