WRAP：无夹具的力矩感知多机器人装配规划
WRAP: Fixtureless Wrench-aware Multi-Robot Assembly Planning
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中文总结 AI 辅助
本文提出WRAP，一种无夹具的力矩感知多机器人装配规划器，通过线性规划推理抓取支撑力，结合启发式搜索与运动规划，实现灵活装配，并在仿真和现实中验证。
中文摘要 AI 辅助
使用机器人进行装配通常需要专门设计的夹具，或依赖于仅自上而下的装配策略。通过使用多个机器人，我们可以避免使用夹具，使机器人装配更加灵活。为多个机器人规划装配序列具有挑战性，因为可能的任务分配和顺序数量众多。此外，我们需要推理装配过程中产生的力，例如，决定是否需要多个机器人进行支撑，或者是否应使用外部支撑（如桌子）。我们提出了Wrap，一种用于多零件装配的多机器人装配规划器，给定零件间的顺序依赖关系、零件网格及其初始状态。我们制定了一个线性规划来推理支撑装配过程中产生的力的有效抓取。搜索利用装配序列，并通过廉价的向后搜索计算启发式，然后在更昂贵的向前搜索中使用该启发式，贪婪地为每个装配步骤找到可行解。然后我们解决多机器人、多目标运动规划问题，为了执行，我们将计划分为接触丰富的装配技能和自由空间运动。我们在多种多零件装配上对规划器进行了基准测试，并将规划器应用于尺寸和运动学不同的机器人组。我们在物理模拟和现实中验证了这项工作。视频和代码可在以下网址获取：此https URL。
英文摘要
Assembly using robots often requires specially designed fixtures, or relies on top-down only assembly strategies. Using multiple robots, we can avoid using fixtures and make robotic assembly more flexible. Planning assembly sequences for multiple robots is challenging due to the high number of possible task assignments and orders. In addition, we need to reason over forces that occur during the assembly process, e.g., to decide if multiple robots are required for support, or if external support such as a table should be used. We present Wrap, a multi-robot assembly planner for multi-part assemblies, given the inter-part ordering-dependencies, the part meshes, and their initial state. We formulate a linear program to reason about valid grasps for supporting the forces that occur during assembly. The search leverages the assembly sequence, and greedily finds a feasible solution per assembly step by computing a heuristic via a cheap backwards search, and using the heuristic in the more expensive forward search. We then solve the multi-robot, multi-goal motion planning problem, and for execution, we split the plan into contact-rich assembly skills, and free space motion. We benchmark the planner on a variety of multi-part assemblies, and apply the planner to groups of robots differing in size and kinematics. We validate the work both in a physics simulation, and in real. Videos and code are available at https://www.vhartmann.com/wrap.
发表机构
- ETH Zurich（苏黎世联邦理工学院）
机构由 AI 辅助整理，请以论文原文为准。
