提出PEEL算法，高效规划长时序拆解路径，解决狭窄通道中的物体拆卸难题。
PEEL: Parallel Extraction for Long-Horizon Disassembly Planning via Scale-Invariant Sampling
- 采用尺度不变采样与多臂老虎机强化探索的RRT算法并行求解
- 在76个单部件拆解任务中达100%成功率，对参数鲁棒性强
- 可应用于含10至17个零件的复杂拆解，适合机器人装配与回收场景
长时序多部件物体拆解要求机器人计算无碰撞的逐件移除运动序列，即使在狭窄逃逸通道中也需可行。为此，本文提出并行提取长时序拆解算法（PEEL），通过采样式运动规划生成物体组件的移除路径，并交付机械臂执行。PEEL采用尺度不变采样策略：先在预热阶段估计物体尺度，再用方向性采样器利用该尺度信息。该策略集成于多臂老虎机快速探索随机树（MAB-RRT）规划器中，依据奖励信号动态切换采样器。通过并行运行多个规划器，PEEL构建有序图以确定部件移除顺序。实验表明，MAB-RRT在76个单部件拆解任务中实现100%成功率，且对参数不敏感；集成到PEEL后，成功解决了4个含10至17个部件的长时序拆解问题，使用Fetch机械臂完成。
原文摘要 · Abstract (English)
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.
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