Non-monotone object rearrangement planning in confined spaces such as cabinets and shelves is a widely occurring but challenging problem in robotics. Both the robot motion and the available regions for object relocation are highly constrained because of the limited space. This work proposes a Multi-Stage Monte Carlo Tree Search (MS-MCTS) method to solve non-monotone object rearrangement planning problems in confined spaces. Our approach decouples the complex problem into simpler subproblems using an object stage topology. A subgoal-focused tree expansion algorithm that jointly considers the high-level planning and the low-level robot motion is designed to reduce the search space and better guide the search process. By fitting the task into the MCTS paradigm, our method produces optimistic solutions by balancing exploration and exploitation. The experiments demonstrate that our method outperforms the existing methods in terms of the planning time, the number of steps, and the total move distance. Moreover, we deploy our MS-MCTS to a real-world robot system and verify its performance in different scenarios.

翻译：非单调物体重排规划在柜体、货架等受限空间中是机器人领域普遍存在且极具挑战性的问题。由于空间限制，机器人运动及物体重排的可用区域均受到高度约束。本文提出一种多阶段蒙特卡洛树搜索（MS-MCTS）方法，用于解决受限空间中的非单调物体重排规划问题。该方法利用物体阶段拓扑将复杂问题分解为若干更简单的子问题。通过设计一种联合考虑高层规划与底层机器人运动的子目标聚焦树扩展算法，有效缩减搜索空间并优化搜索引导过程。通过将任务适配至MCTS范式，该方法在探索与利用之间取得平衡，从而生成乐观解。实验表明，本方法在规划时间、执行步数及总移动距离方面均优于现有方法。此外，我们将所提出的MS-MCTS部署至真实机器人系统，并在不同场景中验证其性能。