PEEL: スケール不変サンプリングによる長期的分解計画の並列抽出
PEEL: Parallel Extraction for Long-Horizon Disassembly Planning via Scale-Invariant Sampling
ロボットによる多部品物体の分解作業を効率的に解くため、スケール不変サンプリングを用いた並列抽出アルゴリズムPEELを提案し、Fetchマニピュレータで長期的な分解問題を解決した。
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著者: Servet B. Bayraktar, Andreas Orthey, Zachary Kingston, Marc Toussaint
分類: cs.RO
原文アブストラクト
Long-horizon multi-part object disassembly requires robots to compute feasible sequences of collision-free removal motions, even in the presence of tight, narrow escape corridors. To efficiently solve such disassembly problems, we propose Parallel Extraction for Long-Horizon Disassembly (PEEL), an algorithm which efficiently computes disassembly motions for object assemblies and feeds them to a robot manipulator for execution. PEEL uses sampling-based motion planning to compute single-object motions through the use of a scale-invariant sampling scheme, where the object scale is estimated in a burn-in phase and a subsequent directional sampler exploits the scale. This sampling scheme is integrated into a multi-arm bandit rapidly-exploring random tree (MAB-RRT) planner, which switches between different samplers depending on the reward signal received. Using MAB-RRT, the PEEL algorithm runs a batch of planners in parallel to obtain an ordered graph specifying the sequence in which object parts have to be removed. We show that MAB-RRT can efficiently solve single-part disassemblies with 100 percent success rate on 76 assemblies, and that it is robust to its parameters. By integrating MAB-RRT into PEEL, we solve four long-horizon disassembly problems using the Fetch manipulator robot involving 10 to 17 individual object parts.