Single-query Path Planning Using Sample-efficient Probability Informed Trees
This work addresses path planning efficiency for robotics or autonomous systems, offering incremental improvements in sample efficiency.
The paper tackles high-dimensional path planning by introducing SPRINT, a sampling-based method that reduces collision check samples using predictive heuristics, resulting in finding shorter or comparable-length paths in significantly less time than common methods.
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.