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arXiv · 1910.05361

Relevant Region Exploration On General Cost-maps For Sampling-Based Motion Planning

Sagar Suhas Joshi·Panagiotis Tsiotras
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Abstract

Asymptotically optimal sampling-based planners require an intelligent exploration strategy to accelerate convergence. After an initial solution is found, a necessary condition for improvement is to generate new samples in the so-called "Informed Set". However, Informed Sampling can be ineffective in focusing search if the chosen heuristic fails to provide a good estimate of the solution cost. This work proposes an algorithm to sample the "Relevant Region" instead, which is a subset of the Informed Set. The Relevant Region utilizes cost-to-come information from the planner's tree structure, reduces dependence on the heuristic, and further focuses the search. Benchmarking tests in uniform and general cost-space settings demonstrate the efficacy of Relevant Region sampling.

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Sagar Suhas Joshi, Panagiotis Tsiotras. 2019-10-11. Relevant Region Exploration On General Cost-maps For Sampling-Based Motion Planning. https://arxiv.org/abs/1910.05361

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