バッチ情報木BIT*：暗黙的ランダム幾何グラフのヒューリスティック探索によるサンプリングベース最適計画
Batch Informed Trees (BIT*): Sampling-based Optimal Planning via the Heuristically Guided Search of Implicit Random Geometric Graphs
グラフ探索とサンプリングベース計画を統合したBIT*を提案し、サンプル集合を暗黙的なランダム幾何グラフとみなしてA*的な効率探索とRRT的な anytime スケーラビリティを両立、確率的完全性と漸近最適性を示した。
著者: Jonathan D. Gammell, Siddhartha S. Srinivasa, Timothy D. Barfoot
分類: cs.RO
原文アブストラクト
In this paper, we present Batch Informed Trees (BIT*), a planning algorithm based on unifying graph- and sampling-based planning techniques. By recognizing that a set of samples describes an implicit random geometric graph (RGG), we are able to combine the efficient ordered nature of graph-based techniques, such as A*, with the anytime scalability of sampling-based algorithms, such as Rapidly-exploring Random Trees (RRT). BIT* uses a heuristic to efficiently search a series of increasingly dense implicit RGGs while reusing previous information. It can be viewed as an extension of incremental graph-search techniques, such as Lifelong Planning A* (LPA*), to continuous problem domains as well as a generalization of existing sampling-based optimal planners. It is shown that it is probabilistically complete and asymptotically optimal. We demonstrate the utility of BIT* on simulated random worlds in $\mathbb{R}^2$ and $\mathbb{R}^8$ and manipulation problems on CMU's HERB, a 14-DOF two-armed robot. On these problems, BIT* finds better solutions faster than RRT, RRT*, Informed RRT*, and Fast Marching Trees (FMT*) with faster anytime convergence towards the optimum, especially in high dimensions.
