Non-parametric Learning to Aid Path Planning over Slopes

— 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.

• Type: Journal
• Year: 2010
• Where: IJRR
• Authors: Sisir Karumanchi, Thomas Allen, Tim Bailey, Steve Scheding
• Keywords: IJRR 2010, Maximum Feasible Speed, Non-parametric Learning Technique, Unmanned Ground Vehicles