サンプル効率を高める確率情報付き木による単一クエリ経路計画
Single-query Path Planning Using Sample-efficient Probability Informed Trees
衝突判定サンプル数を抑えるヒューリスティックを導入し、高次元経路計画を高速かつ堅牢に解くサンプリングベース手法SPRINTを提案した論文。
著者: Daniel Rakita, Bilge Mutlu, Michael Gleicher
分類: cs.RO
原文アブストラクト
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.
