制御バリア関数を用いた適応サンプリングベース動作計画
Adaptive Sampling-based Motion Planning with Control Barrier Functions
RRT*に制御バリア関数を組み合わせて衝突チェックを省略し、クロスエントロピー法による適応サンプリングで効率を高める動作計画手法を提案した。
著者: Ahmad Ahmad, Calin Belta, Roberto Tron
分類: cs.RO, cs.SY, eess.SY
原文アブストラクト
Sampling-based algorithms, such as Rapidly Exploring Random Trees (RRT) and its variants, have been used extensively for motion planning. Control barrier functions (CBFs) have been recently proposed to synthesize controllers for safety-critical systems. In this paper, we combine the effectiveness of RRT-based algorithms with the safety guarantees provided by CBFs in a method called CBF-RRT$^\ast$. CBFs are used for local trajectory planning for RRT$^\ast$, avoiding explicit collision checking of the extended paths. We prove that CBF-RRT$^\ast$ preserves the probabilistic completeness of RRT$^\ast$. Furthermore, in order to improve the sampling efficiency of the algorithm, we equip the algorithm with an adaptive sampling procedure, which is based on the cross-entropy method (CEM) for importance sampling (IS). The procedure exploits the tree of samples to focus the sampling in promising regions of the configuration space. We demonstrate the efficacy of the proposed algorithms through simulation examples.
