商空間RRT：逐次簡略化を用いた動作計画
Rapidly-Exploring Quotient-Space Trees: Motion Planning using Sequential Simplifications
配置空間を低次元の商空間列に射影する逐次簡略化を活用し、商空間上で木を逐次的かつ同時に成長させるQRRTアルゴリズムを提案。確率的完全性を持ち、実行時間を大幅に短縮できることを示した。
著者: Andreas Orthey, Marc Toussaint
分類: cs.RO, cs.AI
原文アブストラクト
Motion planning problems can be simplified by admissible projections of the configuration space to sequences of lower-dimensional quotient-spaces, called sequential simplifications. To exploit sequential simplifications, we present the Quotient-space Rapidly-exploring Random Trees (QRRT) algorithm. QRRT takes as input a start and a goal configuration, and a sequence of quotient-spaces. The algorithm grows trees on the quotient-spaces both sequentially and simultaneously to guarantee a dense coverage. QRRT is shown to be (1) probabilistically complete, and (2) can reduce the runtime by at least one order of magnitude. However, we show in experiments that the runtime varies substantially between different quotient-space sequences. To find out why, we perform an additional experiment, showing that the more narrow an environment, the more a quotient-space sequence can reduce runtime.
