Non-Parametric Informed Exploration for Sampling-Based Motion Planning

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 “$L_{2} -$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.