双方向探索による漸近最適なサンプリングベース運動計画アルゴリズム
An Asymptotically-Optimal Sampling-Based Algorithm for Bi-directional Motion Planning
FMT*を双方向探索に拡張したBFMT*を提案し、遅延探索と漸近最適性を保ちながら単方向版や既存手法より優れることを実験で示した。
著者: Joseph A. Starek, Javier V. Gomez, Edward Schmerling, Lucas Janson, Luis Moreno, Marco Pavone
分類: cs.RO
原文アブストラクト
Bi-directional search is a widely used strategy to increase the success and convergence rates of sampling-based motion planning algorithms. Yet, few results are available that merge both bi-directional search and asymptotic optimality into existing optimal planners, such as PRM*, RRT*, and FMT*. The objective of this paper is to fill this gap. Specifically, this paper presents a bi-directional, sampling-based, asymptotically-optimal algorithm named Bi-directional FMT* (BFMT*) that extends the Fast Marching Tree (FMT*) algorithm to bi-directional search while preserving its key properties, chiefly lazy search and asymptotic optimality through convergence in probability. BFMT* performs a two-source, lazy dynamic programming recursion over a set of randomly-drawn samples, correspondingly generating two search trees: one in cost-to-come space from the initial configuration and another in cost-to-go space from the goal configuration. Numerical experiments illustrate the advantages of BFMT* over its unidirectional counterpart, as well as a number of other state-of-the-art planners.
