Multi-Agent Coordination Framework for Autonomous Delivery Robots in Dense Urban Environments

Abstract

The proliferation of last-mile autonomous delivery fleets requires robust, scalable, and communication-efficient multi-agent coordination frameworks to safely navigate dense urban environments. Traditional multi-agent pathfinding approaches frequently scale poorly under high agent density or suffer severe performance degradation during sudden communication dropouts. To resolve these operational challenges, this paper presents a novel distributed hybrid coordination framework that integrates macroscopic consensus-based task allocation with localized, dynamic conflict resolution strategies. By implementing a decentralized token-passing auction model alongside asynchronous dynamic window path updates, the system guarantees conflict-free trajectories without relying on a persistent, centralized server. Extensive software co-simulations and physical field trials demonstrate that the proposed framework achieves a 22.4% reduction in path conflict frequency and a 16.8% improvement in fleet resource utilization compared to baseline prioritized planning models. These results prove that the system is highly resilient and viable for high-density, real-world autonomous logistics infrastructures.