提出分层优化方法，实现可重构机器人在结构约束下的高效非破坏性拆解。
Hierarchical Planning and Scheduling for Reconfigurable Multi-Robot Disassembly Systems under Structural Constraints
- 分层优化：遗传算法结合约束规划，分步解决任务序列与调度问题。
- 创新初始化策略，降低复杂搜索空间中的局部最优风险。
- 适用于需灵活配置的工业拆解场景，尤其适合结构复杂的设备回收。
本研究提出一种系统集成方法，用于规划可重构机器人的调度、作业序列、任务与运动轨迹，以非破坏性方式自动拆解受结构约束的物体。该系统需根据目标结构动态调整配置与协同方式，但庞大的复杂搜索空间易导致陷入局部最优。为此，将配备多种工具的多机械臂与旋转台集成于可重构平台，基于分层优化方法，在现实时间范围内生成满足多重优先条件且符合强制要求的作业计划。方法采用两个多目标遗传算法进行任务序列与任务规划，并引入运动评估，随后通过约束规划完成调度。由于序列规划搜索空间远大于任务规划，本文提出一种针对约束结构设计的染色体初始化方法，有效缓解局部最优风险。仿真结果表明，所提方法能有效解决可重构机器人拆解中的复杂问题。
原文摘要 · Abstract (English)
This study presents a system integration approach for planning schedules, sequences, tasks, and motions for reconfigurable robots to automatically disassemble constrained structures in a non-destructive manner. Such systems must adapt their configuration and coordination to the target structure, but the large and complex search space makes them prone to local optima. To address this, we integrate multiple robot arms equipped with different types of tools, together with a rotary stage, into a reconfigurable setup. This flexible system is based on a hierarchical optimization method that generates plans meeting multiple preferred conditions under mandatory requirements within a realistic timeframe. The approach employs two many-objective genetic algorithms for sequence and task planning with motion evaluations, followed by constraint programming for scheduling. Because sequence planning has a much larger search space, we introduce a chromosome initialization method tailored to constrained structures to mitigate the risk of local optima. Simulation results demonstrate that the proposed method effectively solves complex problems in reconfigurable robotic disassembly.
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