针对AGV仓库路径规划，提出更贴近现实的多智能体路径寻优方法。
From Gridworlds to Warehouses: Adapting Lightweight One-shot Multi-Agent Pathfinding for AGVs
- 约束旋转为多步动作、加减速和禁止追尾碰撞，模拟真实AGV行为
- 基于PIBT的算法在多智能体场景下表现最佳，兼顾效率与可扩展性
- 适用于需高精度路径规划的自动化仓储系统设计与优化
单次规划下的多智能体路径规划（MAPF）是仓库自动化的核心，但传统方法通常假设四连通二维网格中单位时间四方向移动。为弥合现实差距并保持离散组合搜索的可追踪性，本文提出面向差速驱动AGV的多智能体仓库路径规划（MAWPF），包含四项约束：(i) 智能体仅允许直线运动和原地旋转；(ii) 旋转需多步代价；(iii) 考虑加速与减速；(iv) 禁止跟随碰撞以防止追尾事故。为高效求解MAWPF，我们适配了代表性次优算法PP、LNS2、PIBT和LaCAM，并进行综合基准测试。实验表明，PP与LNS2在多智能体实例中表现不佳，而基于PIBT的方法在扩展性上更具优势，尽管解的质量有所上升。这些结果标志着将经典网格世界MAPF向实际仓库部署迈出重要一步。
原文摘要 · Abstract (English)
Multi-agent pathfinding (MAPF) under one-shot planning is a core component of warehouse automation, yet classical formulations typically assume four-connected 2D grids with unit-time moves in four directions. To fill reality gaps while still being trackable with discrete combinatorial search, this work proposes a more practical counterpart tailored to differential-drive AGVs. We term this multi-agent warehouse pathfinding (MAWPF), featured with four constraints: (i) agent actions are restricted to straight motion and in-place rotation; (ii) rotations require multi-step costs; (iii) acceleration and deceleration are considered, and; (iv) follower collisions are prohibited to prevent rear-end crashes. To solve MAWPF efficiently, we adapt representative suboptimal MAPF algorithms-PP, LNS2, PIBT, and LaCAM-and conduct comprehensive benchmarking. Our experiments reveal that PP and LNS2 struggle to solve instances with many agents, while PIBT-based approaches achieve preferable scalability with increased solution cost. We believe that these constitute an important step toward adapting classical gridworld MAPF to operational warehouse setups.
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