動作計画におけるガイド空間の評価
Evaluating Guiding Spaces for Motion Planning
動作計画アルゴリズムのサンプリングバイアスを「ガイド空間」として統一的に定義し、情報理論的な評価手法を提案して複数のアルゴリズムを分析した。
著者: Amnon Attali, Stav Ashur, Isaac Burton Love, Courtney McBeth, James Motes, Diane Uwacu, Marco Morales, Nancy M. Amato
分類: cs.RO, cs.AI
原文アブストラクト
Randomized sampling based algorithms are widely used in robot motion planning due to the problem's intractability, and are experimentally effective on a wide range of problem instances. Most variants do not sample uniformly at random, and instead bias their sampling using various heuristics for determining which samples will provide more information, or are more likely to participate in the final solution. In this work, we define the \emph{motion planning guiding space}, which encapsulates many seemingly distinct prior works under the same framework. In addition, we suggest an information theoretic method to evaluate guided planning which places the focus on the quality of the resulting biased sampling. Finally, we analyze several motion planning algorithms in order to demonstrate the applicability of our definition and its evaluation.
