ReloPush：使用非完整约束移动机器人推动器在受限空间中进行多物体重排
ReloPush: Multi-object Rearrangement in Confined Spaces with a Nonholonomic Mobile Robot Pusher
AI总结：
ReloPush通过构建Push-Traversability图统一表示几何、运动学与物理约束，将多物体重排分解为单物体推动序列，在密集杂乱受限空间中实现比基线快数个数量级且更稳健的非完整移动机器人推动规划。
摘要：
我们专注于使用非完整约束移动机器人的基于推动的多物体重排规划。同时存在的几何约束、运动学约束和物理约束使这一问题尤其具有挑战性。先前关于重排规划的工作通常通过假设灵巧硬件、抓取式操作或稀疏占用的工作空间来放宽其中一些约束。我们的关键见解是，通过将这些约束捕获到一个统一表示中，我们可以赋予受约束的机器人通过以有利于自身的方式修改环境来处理困难问题实例的能力。为此，我们引入了Push-Traversability图，其顶点表示机器人可以从其推动物体的位姿，边表示这些位姿之间最优的、运动学可行的且稳定的转移。基于该图，我们开发了ReloPush，这是一个基于图的规划框架，它接收复杂的多物体重排任务作为输入，并将其分解为一系列单物体推动任务。我们在一系列具有挑战性的场景中评估了ReloPush，这些场景涉及使用1/10比例的机器人赛车对多达九个物体的密集杂乱工作空间进行重排。与两个缺乏我们所提出的图结构的基线相比，ReloPush展现出数量级更快的运行时间，并且在真实世界中执行显著更稳健，这体现在更低的执行时间和更少的物体接触丢失上。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
