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.
@article{arxiv.1910.05361,
title = {Relevant Region Exploration On General Cost-maps For Sampling-Based Motion Planning},
author = {Sagar Suhas Joshi and Panagiotis Tsiotras},
journal= {arXiv preprint arXiv:1910.05361},
year = {2021}
}