发表机构
Technical University of Berlin; Robotics Institute Germany (RIG)（柏林工业大学; 德国机器人研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对大规模装配体拆卸的多机器人规划问题，提出CoMuDi方法，集成ST-RRT*规划器，经多组装配体实验验证其能最小化完工时间且成功率高、空闲时间短。
AI 中文摘要
多机器人拆卸任务的任务与运动规划要求机器人在受限工作空间内作业，同时与其他机器人协调运动。为解决该问题，我们提出一种名为协同多机器人拆卸（CoMuDi）的规划方法，用于协调机器人团队执行拆卸任务。输入为机器人团队、对象装配体及依赖图，基于此我们创建拾取、放置和退出动作的复合任务。通过传播时间约束，我们确保每个机器人能尽可能早地开始和结束任务，同时避免与附近机器人发生碰撞。我们将时空RRT*规划器（ST-RRT*）集成到CoMuDi中，以确保单个任务的到达时间最小化，从而帮助我们最小化整体完工时间。我们使用ST-RRT*和RRT*规划器在不同时间边界下对比CoMuDi的性能，结果表明CoMuDi与ST-RRT*的组合能在最小化完工时间的同时实现更高的成功率。最后，我们对包含最多49个零件、最多9个机器人的6种装配体评估CoMuDi，结果显示其生成的机器人路径空闲时间短，可可靠解决大规模装配体的拆卸问题。
英文摘要
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.
Comments16 pages, 13 figures
