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

Path planning on a terrain cost-map. White areas represent the high-cost rough spaces.


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.