Single-query Path Planning Using Sample-efficient Probability Informed Trees

TL;DR

SPRINT is a novel sampling-based path planning algorithm that efficiently finds solutions in high-dimensional spaces by using heuristics to minimize collision checks, leading to faster and comparable or shorter paths.

Contribution

The paper introduces SPRINT, a new path planning method that significantly reduces sampling requirements through heuristics, improving efficiency in high-dimensional problems.

Findings

Finds shorter or comparable paths faster than existing methods

Reduces collision check samples significantly

Achieves high efficiency in high-dimensional planning

Abstract

In this work, we present a novel sampling-based path planning method, called SPRINT. The method finds solutions for high dimensional path planning problems quickly and robustly. Its efficiency comes from minimizing the number of collision check samples. This reduction in sampling relies on heuristics that predict the likelihood that samples will be useful in the search process. Specifically, heuristics (1) prioritize more promising search regions; (2) cull samples from local minima regions; and (3) steer the search away from previously observed collision states. Empirical evaluations show that our method finds shorter or comparable-length solution paths in significantly less time than commonly used methods. We demonstrate that these performance gains can be largely attributed to our approach to achieve sample efficiency.