多机器人协同拆解，高效规划路径降低整体耗时。
Coordinated Multi-Robot Disassembly for Makespan Optimization of Large-Scale Assemblies
- 构建任务依赖图，生成可并行的抓取、放置与退出动作。
- 结合时空RRT*算法，使各机器人尽早完成任务且避免碰撞。
- 在最多49个零件、9台机器人场景下表现稳定，空闲时间低。
针对多机器人在狭小空间内协同拆解大型装配体的任务，提出一种名为协同多机器人拆解（CoMuDi）的规划方法。输入包括机器人团队、装配体结构及依赖关系图，基于此生成复合任务（抓取、放置、退出）。通过传播时间约束，确保每个机器人尽可能早地开始和结束任务，同时避免与其他机器人发生碰撞。将时空RRT*（ST-RRT*）集成到CoMuDi中，使单个任务的到达时间最小化，从而优化整体完成时间（makespan）。对比使用ST-RRT*与传统RRT*在不同时间限制下的表现，结果显示二者结合能显著提升成功率并减少完成时间。在包含最多49个部件和9台机器人的六个实际场景中验证，CoMuDi生成的路径具有低空闲时间，表明其在大规模装配体拆解中具备可靠性和高效性。
原文摘要 · Abstract (English)
Multi-robot task and motion planning for disassembly tasks requires robots to operate in confined workspaces while coordinating their motions with other robots. To tackle this problem, we propose a planning method called coordinated multi-robot disassembly (CoMuDi). CoMuDi coordinates a team of robots for disassembly tasks. The input is a team of robots, an assembly of objects, and a dependency graph. Based on this information, we create compound tasks for pick, place, and exit motions. By propagating temporal constraints, we ensure that each robot can start and end their tasks as early as possible while avoiding collisions with nearby robots. By integrating the space-time RRT* planner (ST-RRT*) into CoMuDi, we ensure that individual tasks minimize arrival time and thereby help us minimize overall makespan. We compare the performance of CoMuDi using both ST-RRT* and RRT* planners with varying time bounds, demonstrating that the combination of CoMuDi and ST-RRT* leads to a higher success rate while minimizing makespan. Finally, we evaluate CoMuDi on six assemblies with up to 49 pieces and up to 9 robots. In those scenarios, we show that CoMuDi returns robot paths that exhibit low idle times, thereby demonstrating that CoMuDi can reliably solve large-scale assemblies.
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