HiRAD: A Flexible Large-Scale AGV Routing System

• URL: http://arxiv.org/abs/2609.09752v1
• Date: Wed, 09 Sep 2026 05:50:17 GMT
• Title: HiRAD: A Flexible Large-Scale AGV Routing System
• Abstract summary: We propose HiRAD, a hierarchical RL framework for continuous-space AGV routing with real-time guarantees.<n>HiRAD reduces makespan by 45 percent to 63 percent and shortens end-to-end runtime.
• Score: 6.059667257410882
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Automatic Guided Vehicles (AGVs) substantially boost warehouse throughput, but routing large-scale AGV fleets remains challenging. Classical Multi-Agent Pathfinding solvers suffer from exploding combinatorial complexity and super-quadratic runtime, while relying on idealized grid or piecewise-linear motion models that mismatch real-world kinematics. Recent Reinforcement Learning (RL) solutions improve flexibility via decentralized agent policies but depend on discretized spatiotemporal representations, require millions of episodes to converge, and incur full-map observation at every step, which leads to large models, slow convergence, and high inference latency that violates real-time industrial control constraints. To address these bottlenecks, we propose HiRAD, a hierarchical RL framework for continuous-space AGV routing with real-time guarantees: (1) a step-level spatiotemporal representation that translates continuous motion into a differentiable RL problem, (2) a hierarchical strategy that splits heading choice from velocity control to reduce the action space, and (3) an asynchronous event-driven decision pipeline that lowers inference complexity from O(n^2) to O(n) and cuts per-step latency by as much as 71 percent. Across random graphs and two warehouse maps, HiRAD reduces makespan by 45 percent to 63 percent and shortens end-to-end runtime.

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