We focus on push-based multi-object rearrangement planning using a nonholonomically constrained mobile robot. The simultaneous geometric, kinematic, and physics constraints make this problem especially challenging. Prior work on rearrangement planning often relaxes some of these constraints by assuming dexterous hardware, prehensile manipulation, or sparsely occupied workspaces. Our key insight is that by capturing these constraints into a unified representation, we could empower a constrained robot to tackle difficult problem instances by modifying the environment in its favor. To this end, we introduce a Push-Traversability graph, whose vertices represent poses that the robot can push objects from, and edges represent optimal, kinematically feasible, and stable transitions between them. Based on this graph, we develop ReloPush, a graph-based planning framework that takes as input a complex multi-object rearrangement task and breaks it down into a sequence of single-object pushing tasks. We evaluate ReloPush across a series of challenging scenarios, involving the rearrangement of densely cluttered workspaces with up to nine objects, using a 1/10-scale robot racecar. ReloPush exhibits orders of magnitude faster runtimes and significantly more robust execution in the real world, evidenced in lower execution times and fewer losses of object contact, compared to two baselines lacking our proposed graph structure.

翻译：本文研究基于推杆操作的多目标重排规划问题，采用受非完整约束的移动机器人。同时存在的几何约束、运动学约束与物理约束使得该问题极具挑战性。现有重排规划研究通常通过假设灵巧硬件、抓取式操作或稀疏工作空间来放松部分约束。我们的核心见解是：通过将这些约束整合为统一表征，可使受约束机器人通过优化环境配置来处理复杂问题实例。为此，我们提出推杆可通行图，其顶点表示机器人可推动物体的位姿，边表示顶点间最优、运动学可行且稳定的状态转移。基于此图结构，我们开发了ReloPush——一种图规划框架，该框架将复杂的多目标重排任务分解为序列化的单目标推杆子任务。我们使用1/10比例遥控赛车，在包含最多九个物体的密集杂乱工作空间中进行系列挑战性场景测试。实验表明：相较于两种未采用本图结构的基线方法，ReloPush在现实世界中展现出数量级级的运行速度提升与显著更强的执行鲁棒性，具体体现为更短的任务执行时间与更少的物体接触丢失现象。