作者：Dhruv Saxena Maxim Likhachev
我们对拾取和放置式机器人操作任务感兴趣，包括可移动物体之间的受限3D工作空间，这些可移动物体可能会被机器人重新布置，并可能滑动、倾斜、倾斜或倾倒。最近提出的M4M算法通过解决该问题的多代理路径查找MAPF抽象来确定哪些对象需要移动以及移动到哪里。然后，它使用一个不可理解的推送规划器来计算机器人如何实现这些重新排列的动作，并使用刚体物理模拟器来检查动作是否满足问题中编码的物理约束。然而，M4M贪婪地致力于在规划过程中发现的有效推送，并且如果需要重新排列多个对象，则不会对推送排序提出理由。此外，M4M不会对其他可能导致不同重排和推送的MAPF解决方案进行推理。本文在扩展M4M的基础上，提出了增强M4M（E-M4M）——一种基于图搜索的系统求解器
We are interested in pick-and-place style robot manipulation tasks incluttered and confined 3D workspaces among movable objects that may berearranged by the robot and may slide, tilt, lean or topple. A recentlyproposed algorithm, M4M, determines which objects need to be moved and where bysolving a Multi-Agent Pathfinding MAPF abstraction of this problem. It thenutilises a nonprehensile push planner to compute actions for how the robotmight realise these rearrangements and a rigid body physics simulator to checkwhether the actions satisfy physics constraints encoded in the problem.However, M4M greedily commits to valid pushes found during planning, and doesnot reason about orderings over pushes if multiple objects need to berearranged. Furthermore, M4M does not reason about other possible MAPFsolutions that lead to different rearrangements and pushes. In this paper, weextend M4M and present Enhanced-M4M (E-M4M) — a systematic graph search-basedsolver that searches over orderings of pushes for movable objects that need tobe rearranged and different possible rearrangements of the scene. We introduceseveral algorithmic optimisations to circumvent the increased computationalcomplexity, discuss the space of problems solvable by E-M4M and show thatexperimentally, both on the real robot and in simulation, it significantlyoutperforms the original M4M algorithm, as well as other state-of-the-artalternatives when dealing with complex scenes.
论文链接：http://arxiv.org/pdf/2303.13385v1
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