サンプリングと分割軌道最適化による長距離運動計画
Long-Horizon Motion Planning via Sampling and Segmented Trajectory Optimization
RRT*で準最適な初期経路を素早く生成し、軌道を分割して最適化を繰り返すことで、障害物環境での衝突回避かつ動的に実行可能な長距離運動計画を高速に得るハイブリッドプランナを提案した。
著者: Jessica Leu, Michael Wang, Masayoshi Tomizuka
分類: cs.RO
原文アブストラクト
This paper presents a hybrid robot motion planner that generates long-horizon motion plans for robot navigation in environments with obstacles. We propose a hybrid planner, RRT* with segmented trajectory optimization (RRT*-sOpt), which combines the merits of sampling-based planning, optimization-based planning, and trajectory splitting to quickly plan for a collision-free and dynamically-feasible motion plan. When generating a plan, the RRT* layer quickly samples a semi-optimal path and sets it as an initial reference path. Then, the sOpt layer splits the reference path and performs optimization on each segment. It then splits the new trajectory again and repeats the process until the whole trajectory converges. We also propose to reduce the number of segments before convergence with the aim of further reducing computation time. Simulation results show that RRT*-sOpt benefits from the hybrid structure with trajectory splitting and performs robustly in various robot platforms and scenarios.
