提出可自适应调整规划时长的闭环多智能体路径算法，兼顾效率与可靠性。
Adaptive-Horizon Conflict-Based Search for Closed-Loop Multi-Agent Path Finding
- 基于冲突搜索构建闭环框架，动态调整规划时长以匹配算力预算。
- 在有限算力下快速生成可行解，算力充足时渐近最优。
- 适合对实时性与安全性要求高的自动化仓储等场景。
多智能体路径规划（MAPF）是自动化仓库与物流中大规模机器人集群协同的核心问题。现有方法通常为开环规划器，需预先计算完整轨迹，导致执行前延迟较高；或为无可靠性能保障的闭环启发式方法，限制其在安全关键场景的应用。本文提出任意时间闭环冲突搜索（ACCBS），基于有限时域冲突搜索（CBS）变体，并借鉴模型预测控制（MPC）中的迭代时域加深思想，引入时域自适应机制。ACCBS根据可用计算资源动态调整规划时长，并复用单一约束树实现不同时间窗间的无缝切换。结果表明，该算法能在短时间内生成高质量可行解，且随计算资源增加趋于最优，具备任意时间特性。大量案例研究显示，ACCBS在计算效率、解质量与执行灵活性之间取得良好平衡，其闭环结构天然支持在线扰动处理。
原文摘要 · Abstract (English)
Multi-Agent Path Finding (MAPF) is a core coordination problem for large robot fleets in automated warehouses and logistics. Existing approaches are typically either open-loop planners, which must compute complete trajectories before execution and therefore may incur substantial planning latency before actions can be taken, or closed-loop heuristics without reliable performance guarantees, limiting their use in safety-critical deployments. This paper presents Anytime Closed-Loop Conflict-Based Search (ACCBS), a closed-loop algorithm built on a finite-horizon variant of Conflict-Based Search (CBS) with a horizon-changing mechanism inspired by iterative horizon-deepening in Model Predictive Control (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 achieves a favorable balance between computational efficiency, solution quality, and execution flexibility, while naturally accommodating online disturbances through its closed-loop formulation.
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