混合任务与运动规划与反应碰撞处理用于多机器人复杂产品拆解：应用于电动汽车电池
Hybrid Task and Motion Planning with Reactive Collision Handling for Multi-Robot Disassembly of Complex Products: Application to EV Batteries
AI总结：
本文提出一种基于视觉的任务与运动规划框架，用于多机器人协同拆解复杂产品，通过学习型RRT规划器和混合安全层提高路径紧凑性和安全性，实验显示在电动汽车电池拆解中显著降低路径长度和扫过体积。
摘要：
本文针对多机器人协调执行复杂操作序列的问题，提出了一种基于视觉的任务与运动规划（TAMP）框架，适用于真实双代理平台。该框架集成了任务分解与分配，并结合基于学习的RRT规划器。一个基于GMM的运动规划器与混合安全层相结合，该层结合了MoveIt/FCL数字双中的预测碰撞检查与反应视觉避障和重新规划。这种整合具有挑战性，因为系统必须同时满足任务优先级、几何可行性、动态障碍物避障和双臂协调约束。该框架通过重复场景扫描和完成状态跟踪更新剩余任务序列，而非执行固定开环计划。在电动汽车电池拆解实验中，与Default-RRTConnect相比，所提系统将累积末端执行器路径长度从48.8减少到17.9 m（-63.3%），改进了makespan从467.9到429.8 s（-8.1%），并减少了扫过体积（R1: 0.583→0.139 m³，R2: 0.696→0.252 m³）和重叠（0.064→0.034 m³）。这些结果表明，在真实双臂拆解场景中结合预测规划和反应碰撞避障，能够提高运动紧凑性、安全性和向更广泛多机器人连续操作任务的扩展性。
英文摘要：
This paper addresses the problem of multi-robot coordination for complex manipulation task sequences. We present a vision-driven task-and-motion planning (TAMP) framework for a real dual-agent platform that integrates task decomposition and allocation with a learning-based RRT planner. A GMM-informed motion planner is coupled with a hybrid safety layer that combines predictive collision checking in a MoveIt/FCL digital twin with reactive vision-based avoidance and replanning. This integration is challenging as the system jointly satisfies task precedence, geometric feasibility, dynamic obstacle avoidance, and dual-arm coordination constraints. The framework operates in closed loop by updating the remaining task sequence from repeated scene scans and completion-state tracking rather than executing a fixed open-loop plan. In EV battery disassembly experiments, compared with Default-RRTConnect under identical perception and task assignments, the proposed system reduces cumulative end-effector path length from 48.8 to 17.9~m ($-63.3\%$), improves makespan from 467.9 to 429.8~s ($-8.1\%$), and reduces swept volumes (R1: $0.583\rightarrow0.139\,\mathrm{m}^3$, R2: $0.696\rightarrow0.252\,\mathrm{m}^3$) and overlap ($0.064\rightarrow0.034\,\mathrm{m}^3$). These results show that combining predictive planning and reactive collision avoidance in a real dual-arm disassembly scenario improves motion compactness, safety, and scalability to broader multi-robot sequential manipulation tasks.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
