提出可扩展的多智能体路径规划框架，提升动态环境下的效率与适应性。
Neural ATTF: A Scalable Solution to Lifelong Multi-Agent Path Planning
- 用学习的启发式方法加速搜索，结合优先任务匹配优化调度顺序。
- 在多个基准上优于TPTS、LNS-PBS等算法，路径质量更高且计算更快。
- 适合需要实时响应的复杂场景，如智能仓储与物流系统。
多智能体拾取与配送（MAPD）是机器人领域的重要问题，尤其在仓库自动化和物流中应用广泛。现有方法在可扩展性、适应性和效率方面存在局限，难以满足动态环境中实时规划的需求。本文提出神经ATTF（Adaptive Task Token Framework），将优先任务匹配（PGTM）模块与神经STA*（Space-Time A*）相结合。神经STA*通过学习到的引导启发式实现快速搜索空间探索，并在动态约束下保证无碰撞。PGTM优先处理延迟任务，动态分配距离最近的代理执行，从而提升连续性与系统吞吐量。在包括TPTS、CENTRAL、RMCA、LNS-PBS、LNS-wPBS在内的多项先进算法对比实验中，神经ATTF展现出更优的可扩展性、解质量与计算效率，证明其在高需求、不可预测的真实多智能体系统中的应用潜力。
原文摘要 · Abstract (English)
Multi-Agent Pickup and Delivery (MAPD) is a fundamental problem in robotics, particularly in applications such as warehouse automation and logistics. Existing solutions often face challenges in scalability, adaptability, and efficiency, limiting their applicability in dynamic environments with real-time planning requirements. This paper presents Neural ATTF (Adaptive Task Token Framework), a new algorithm that combines a Priority Guided Task Matching (PGTM) Module with Neural STA* (Space-Time A*), a data-driven path planning method. Neural STA* enhances path planning by enabling rapid exploration of the search space through guided learned heuristics and ensures collision avoidance under dynamic constraints. PGTM prioritizes delayed agents and dynamically assigns tasks by prioritizing agents nearest to these tasks, optimizing both continuity and system throughput. Experimental evaluations against state-of-the-art MAPD algorithms, including TPTS, CENTRAL, RMCA, LNS-PBS, and LNS-wPBS, demonstrate the superior scalability, solution quality, and computational efficiency of Neural ATTF. These results highlight the framework's potential for addressing the critical demands of complex, real-world multi-agent systems operating in high-demand, unpredictable settings.
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