Non-Parametric Informed Exploration for Sampling-Based Motion Planning

Abstract

Efficient exploration of the search space is crucial for faster convergence in sampling-based motion planning. An effective sampling method must first concentrate on quickly finding a good initial solution and then focus the search on regions that can potentially improve the current best solution. In this paper, we propose a non-parametric exploration technique that addresses these challenges. The proposed algorithm prioritizes search by utilizing heuristics. After an initial solution is found, the method generates samples in the “informed set”, while leveraging collision data to reduce the number of samples in the obstacle space. We demonstrate the efficiency of the proposed approach with several benchmarking experiments.