Sisir Karumanchi, Thomas Allen, Tim Bailey and Steve Scheding
Non-parametric Learning to Aid Path Planning over Slopes
International Journal of Robotics Research, 2010
DescriptionSelf-supervised non-parametric learning in the space of robot behaviour, trained via proprioceptive sensing of vehicle-environment interaction.
AbstractThis 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 velocity in off-road slopes. In addition, an information theoretic test is proposed to validate a chosen proprioceptive representation (such as slip) for mobility map generation. Results of mobility map generation and its benefits to path planning are shown.
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