ePA*SE: 評価が遅い問題向けのエッジ並列A*探索
ePA*SE: Edge-based Parallel A* for Slow Evaluations
状態展開ではなくエッジ評価を並列化することで、ロボティクスで多い評価コストが大きく変動する問題でも高速に計画できる並列A*アルゴリズムを提案し、最適性保証と2つのロボット計画タスクでの有効性を示した。
著者: Shohin Mukherjee, Sandip Aine, Maxim Likhachev
分類: cs.RO
原文アブストラクト
Parallel search algorithms harness the multithreading capability of modern processors to achieve faster planning. One such algorithm is PA*SE (Parallel A* for Slow Expansions), which parallelizes state expansions to achieve faster planning in domains where state expansions are slow. In this work, we propose ePA*SE (Edge-based Parallel A* for Slow Evaluations) that improves on PA*SE by parallelizing edge evaluations instead of state expansions. This makes ePA*SE more efficient in domains where edge evaluations are expensive and need varying amounts of computational effort, which is often the case in robotics. On the theoretical front, we show that ePA*SE provides rigorous optimality guarantees. In addition, ePA*SE can be trivially extended to handle an inflation weight on the heuristic resulting in a bounded suboptimal algorithm w-ePA*SE (Weighted ePA*SE) that trades off optimality for faster planning. On the experimental front, we validate the proposed algorithm in two different planning domains: 1) motion planning for 3D humanoid navigation and 2) task and motion planning for a dual-arm robotic assembly task. We show that ePA*SE can be significantly more efficient than PA*SE and other alternatives. The open-source code for ePA*SE along with the baselines is available here: https://github.com/shohinm/parallel_search
