Neural ATTF: A Scalable Solution to Lifelong Multi-Agent Path Planning
This addresses scalability and efficiency challenges in robotics applications such as warehouse automation and logistics, though it appears incremental as it builds on existing methods like A* and task matching.
The paper tackles the problem of Multi-Agent Pickup and Delivery (MAPD) in dynamic environments by proposing Neural ATTF, which combines a Priority Guided Task Matching Module with Neural STA* for path planning, resulting in superior scalability, solution quality, and computational efficiency compared to state-of-the-art algorithms like TPTS, CENTRAL, RMCA, LNS-PBS, and LNS-wPBS.
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.