Multi-Agent Path Finding is a fundamental problem in robotics and AI, yet most existing formulations treat planning and execution separately and address variants of the problem in an ad hoc manner. This paper presents a system-level framework for MAPF that integrates planning and execution, generalizes across variants, and explicitly models uncertainties. At its core is the MAPF system, a formal model that casts MAPF as a control design problem encompassing classical and uncertainty-aware formulations. To solve it, we introduce Finite-Horizon Closed-Loop Factorization (FICO), a factorization-based algorithm inspired by receding-horizon control that exploits compositional structure for efficient closed-loop operation. FICO enables real-time responses -- commencing execution within milliseconds -- while scaling to thousands of agents and adapting seamlessly to execution-time uncertainties. Extensive case studies demonstrate that it reduces computation time by up to two orders of magnitude compared with open-loop baselines, while delivering significantly higher throughput under stochastic delays and agent arrivals. These results establish a principled foundation for analyzing and advancing MAPF through system-level modeling, factorization, and closed-loop design.

翻译：多智能体路径规划是机器人与人工智能领域的基础问题，然而现有研究大多将规划与执行分离处理，并以特设方式应对不同问题变体。本文提出一种系统级的多智能体路径规划框架，该框架整合规划与执行环节，泛化适应多种问题变体，并显式建模不确定性。其核心是多智能体路径规划系统——一个将多智能体路径规划转化为控制设计问题的形式化模型，涵盖经典形式与不确定性感知形式。为解决该问题，我们提出有限时域闭环分解方法，这是一种受滚动时域控制启发的基于分解的算法，通过利用组合结构实现高效的闭环操作。FICO 能够实现毫秒级响应的实时决策，同时可扩展至数千智能体规模，并能无缝适应执行阶段的不确定性。大量案例研究表明，相较于开环基准方法，该算法将计算时间降低达两个数量级，并在随机延迟与智能体动态到达场景下实现显著更高的吞吐量。这些成果为通过系统级建模、分解与闭环设计来分析与推进多智能体路径规划研究奠定了理论基础。