Abstract

Path finding is a fundamental, yet computationally expensive problem in robotics navigation. Often times, it is necessary to sacrifice optimality to find a feasible plan given a time constraint due to the search complexity. Dynamic environments may further invalidate current computed plans, requiring an efficient planning strategy that can repair existing solutions. This paper presents a massively parallelizable wavefront-based approach to path planning, running on the GPU, that can efficiently repair plans to accommodate world changes and start movement, without having to restart the wavefront propagation process. In addition, we introduce a termination condition which ensures minimum number of GPU iterations while maintaining strict optimality constraints on search graphs with non-uniform costs.

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Dynamic Search on the GPU

Mubbasir Kapadia, Francisco Garcia, Cory D. Boatright and Norman I. Badler

IEEE/RSJ International Conference on Intelligent Robots and Systems, November 2013
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