[论文解读] Cooperative Task and Motion Planning for Multi-Arm Assembly Systems

Jingkai Chen, Jiaoyang Li+8 more|arXiv (Cornell University)|Mar 4, 2022

Modular Robots and Swarm Intelligence被引用 20

一句话总结

本文提出一个分层任务与运动规划框架，将基于 MILP 的高层任务分配与具备碰撞感知的低层多代理路径搜索相结合，以安全高效地组装复杂的多机器人结构，并在多个领域中演示，对象数量最多为 23 个。

ABSTRACT

Multi-robot assembly systems are becoming increasingly appealing in manufacturing due to their ability to automatically, flexibly, and quickly construct desired structural designs. However, effectively planning for these systems in a manner that ensures each robot is simultaneously productive, and not idle, is challenging due to (1) the close proximity that the robots must operate in to manipulate the structure and (2) the inherent structural partial orderings on when each part can be installed. In this paper, we present a task and motion planning framework that jointly plans safe, low-makespan plans for a team of robots to assemble complex spatial structures. Our framework takes a hierarchical approach that, at the high level, uses Mixed-integer Linear Programs to compute an abstract plan comprised of an allocation of robots to tasks subject to precedence constraints and, at the low level, builds on a state-of-the-art algorithm for Multi-Agent Path Finding to plan collision-free robot motions that realize this abstract plan. Critical to our approach is the inclusion of certain collision constraints and movement durations during high-level planning, which better informs the search for abstract plans that are likely to be both feasible and low-makespan while keeping the search tractable. We demonstrate our planning system on several challenging assembly domains with several (sometimes heterogeneous) robots with grippers or suction plates for assembling structures with up to 23 objects involving Lego bricks, bars, plates, or irregularly shaped blocks.

研究动机与目标

• 推动机器人组装的灵活自动化，减少人工重新编程需求。
• 开发一个框架，联合优化多机器人任务分配与无碰撞运动。
• 将关键的碰撞与时长信息纳入高层规划，以提高可行性和效率。
• 证明其在异构机器人团队于近距离作业中组装复杂结构的适用性。

提出的方法

• 使用模式图描述机器人操作过程和先后约束任务。
• 为每个机器人生成多模态路线图，以捕捉可行的移动及与对象的交互。
• 求解一个放松的 MILP，将关键碰撞与先行约束纳入任务分配给机器人。
• 在路线图之间标注潜在碰撞，并使用基于优先级的解耦 MAPF 风格搜索（PBS-AT）生成无碰撞的运动。
• 从 MILP 解决方案中提取子计划序列，并应用带有优先级的迭代路径规划方法，结合 rSIPP 以处理移动障碍和先行约束。

实验结果

研究问题

• RQ1 如何将多臂装配中的任务分配与运动规划整合，以最小化总工时？
• RQ2 哪些高层约束（如碰撞、先行/优先关系）对于确保低层计划的可行性与无碰撞性至关重要？
• RQ3 带有基于 MILP 的任务分配和 MAPF 启发式规划的分层方法是否可扩展到具有多机器人和多对象的复杂装配？
• RQ4 在高层纳入碰撞感知的、与任务相关的信息如何影响计划的可行性与性能？

主要发现

• 该框架在多域中为组装多达 23 个对象的机器人团队产生安全且低工时的计划。
• 在高层规划中包含碰撞约束和移动时长信息可提高可行性，并引导搜索朝着低工时解前进。
• 基于 MILP 的任务分配在尊重先行关系和路线图连通性的同时，能有效将任务映射给机器人。
• PBS-AT 算法将基于优先级的搜索扩展到处理已分配任务和移动障碍，产生无碰撞、遵循先行约束的计划。
• 路线图生成与标注步骤通过重用碰撞信息并将问题分解为可处理的子问题，实现可扩展的规划。