Neural ATTF: A Scalable Solution to Lifelong Multi-Agent Path Planning

2025-04-21

AI Summary

Existing approaches to lifelong multi-agent path finding (MAPF) in dynamic warehouse logistics suffer from critical bottlenecks in scalability, real-time adaptability, and planning efficiency. To address these challenges, this paper proposes the Adaptive Task Token Framework (ATTF), the first method to jointly optimize latency-sensitive task scheduling and collision-aware path planning by integrating Priority-Guided Task Matching (PGTM) with a data-driven Neural Space-Time A* (STA*) algorithm. ATTF incorporates reinforcement learning–inspired heuristics, spatiotemporal A* search, dynamic priority scheduling, and online task reassignment. Evaluated on standard benchmarks, ATTF significantly outperforms state-of-the-art methods—including TPTS, CENTRAL, and LNS-wPBS—achieving a 32% increase in system throughput, a 47% reduction in average planning latency, and enabling millisecond-scale coordinated planning for over one hundred agents.

Technology Category

• Planning, Routing, and Scheduling: Learning for Planning and Scheduling
• Multiagent Systems: Multiagent Planning
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Agentic search
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

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.

Problem

• Scalable lifelong multi-agent path planning in dynamic environments
• Efficient task assignment and collision avoidance for MAPD
• Improving adaptability and throughput in real-time logistics systems

Innovation

• Combines PGTM Module with Neural STA*
• Uses guided learned heuristics for path planning
• Prioritizes delayed agents and nearest tasks