MAPF is a core coordination problem for large robot fleets in automated warehouses and logistics. Existing approaches are typically either open-loop planners, which generate fixed trajectories and struggle to handle disturbances, or closed-loop heuristics without reliable performance guarantees, limiting their use in safety-critical deployments. This paper presents ACCBS, a closed-loop algorithm built on a finite-horizon variant of CBS with a horizon-changing mechanism inspired by iterative deepening in MPC. ACCBS dynamically adjusts the planning horizon based on the available computational budget, and reuses a single constraint tree to enable seamless transitions between horizons. As a result, it produces high-quality feasible solutions quickly while being asymptotically optimal as the budget increases, exhibiting anytime behavior. Extensive case studies demonstrate that ACCBS combines flexibility to disturbances with strong performance guarantees, effectively bridging the gap between theoretical optimality and practical robustness for large-scale robot deployment.

翻译：多智能体路径规划是自动化仓库与物流系统中大规模机器人集群协调的核心问题。现有方法通常分为两类：开环规划器生成固定轨迹但难以应对动态扰动，或闭环启发式算法缺乏可靠性能保证，限制了其在安全关键场景中的应用。本文提出ACCBS算法，该闭环算法基于有限时域CBS变体构建，并采用受模型预测控制中迭代深化思想启发的时域调整机制。ACCBS根据可用计算资源动态调整规划时域，通过复用单一约束树实现不同时域间的无缝切换。该算法能在有限预算内快速生成高质量可行解，并随着计算资源增加渐近收敛至最优解，具备随时终止特性。大量案例研究表明，ACCBS兼具扰动适应性与严格性能保证，有效弥合了大规模机器人部署中理论最优性与实际鲁棒性之间的鸿沟。