We are interested in pick-and-place style robot manipulation tasks in cluttered and confined 3D workspaces among movable objects that may be rearranged by the robot and may slide, tilt, lean or topple. A recently proposed algorithm, M4M, determines which objects need to be moved and where by solving a Multi-Agent Pathfinding MAPF abstraction of this problem. It then utilises a nonprehensile push planner to compute actions for how the robot might realise these rearrangements and a rigid body physics simulator to check whether the actions satisfy physics constraints encoded in the problem. However, M4M greedily commits to valid pushes found during planning, and does not reason about orderings over pushes if multiple objects need to be rearranged. Furthermore, M4M does not reason about other possible MAPF solutions that lead to different rearrangements and pushes. In this paper, we extend M4M and present Enhanced-M4M (E-M4M) -- a systematic graph search-based solver that searches over orderings of pushes for movable objects that need to be rearranged and different possible rearrangements of the scene. We introduce several algorithmic optimisations to circumvent the increased computational complexity, discuss the space of problems solvable by E-M4M and show that experimentally, both on the real robot and in simulation, it significantly outperforms the original M4M algorithm, as well as other state-of-the-art alternatives when dealing with complex scenes.

翻译：我们关注的是在杂乱且受限的三维工作空间中，机器人对可移动物体进行抓取-放置式操作任务，这些物体可能被机器人重新排列，并可能发生滑动、倾斜、倚靠或倾倒。近期提出的M4M算法通过将问题抽象为多智能体路径规划（MAPF）来确定需要移动哪些物体及其放置位置。该算法随后利用非抓取式推动规划器计算机器人实现这些重排所需的动作，并通过刚体物理仿真器检验这些动作是否满足问题设定的物理约束。然而，M4M在规划过程中会贪心地采纳有效推动方案，且在需要重排多个物体时，不涉及推动顺序的推理。此外，M4M也不考虑可能导致不同重排和推动方案的其他MAPF解。本文在M4M基础上提出增强型M4M（E-M4M）——一种基于系统性图搜索的求解器，可对需要重排的可移动物体的推动顺序及场景的不同重排方案进行全局搜索。我们引入多项算法优化以应对计算复杂度的提升，讨论了E-M4M可求解的问题空间，并实验证明：在真实机器人平台及仿真环境中，当处理复杂场景时，该算法显著优于原始M4M算法及其他现有先进替代方案。