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 regarding 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 confined environments.

翻译：在橱柜和货架等受限空间中进行非单调物体重排规划是机器人领域常见但具有挑战性的问题。由于空间有限，机器人的运动以及用于物体重新放置的区域都受到高度约束。本文提出了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。我们的方法通过物体阶段拓扑将复杂问题分解为更简单的子问题。设计了一种联合考虑高层规划与低层机器人运动的子目标聚焦树扩展算法，以减少搜索空间并更好地引导搜索过程。通过将任务适配到MCTS范式，我们的方法通过平衡探索与利用生成乐观解。实验证明，该方法在规划时间、步骤数和总移动距离方面均优于现有方法。此外，我们将MS-MCTS部署到真实机器人系统中，并在不同受限环境中验证了其性能。