Object rearrangement planning in complex, cluttered environments is a common challenge in warehouses, households, and rescue sites. Prior studies largely address monotone instances, whereas real-world tasks are often non-monotone-objects block one another and must be temporarily relocated to intermediate positions before reaching their final goals. In such settings, effective multi-agent collaboration can substantially reduce the time required to complete tasks. This paper introduces Centralized, Asynchronous, Multi-agent Monte Carlo Tree Search (CAM-MCTS), a novel framework for general-purpose makespan-efficient object rearrangement planning in challenging environments. CAM-MCTS combines centralized task assignment-where agents remain aware of each other's intended actions to facilitate globally optimized planning-with an asynchronous task execution strategy that enables agents to take on new tasks at appropriate time steps, rather than waiting for others, guided by a one-step look-ahead cost estimate. This design minimizes idle time, prevents unnecessary synchronization delays, and enhances overall system efficiency. We evaluate CAM-MCTS across a diverse set of monotone and non-monotone tasks in cluttered environments, demonstrating consistent reductions in makespan compared to strong baselines. Finally, we validate our approach on a real-world multi-agent system under different configurations, further confirming its effectiveness and robustness.

翻译：在复杂、杂乱的仓库、家庭及救援现场环境中，对象重排规划是一项普遍存在的挑战。现有研究主要针对单调实例，而实际任务通常是非单调的——对象相互阻挡，必须临时移至中间位置才能到达最终目标。在此类场景中，有效的多智能体协作能显著缩短任务完成时间。本文提出集中式异步多智能体蒙特卡洛树搜索（CAM-MCTS），这是一种适用于挑战性环境中通用型完工时间高效对象重排规划的新框架。CAM-MCTS融合了集中式任务分配（使智能体能够感知彼此的计划行动以促进全局优化规划）与异步任务执行策略（通过单步前瞻成本估计引导智能体在适当时机承担新任务，而非等待其他智能体）。该设计最大限度地减少了空闲时间，避免了不必要的同步延迟，并提升了整体系统效率。我们在杂乱环境中的多种单调与非单调任务上评估CAM-MCTS，结果表明相较于现有强基线方法，其能持续缩短完工时间。最后，我们在不同配置的真实多智能体系统上验证了所提方法，进一步证实了其有效性与鲁棒性。