HiRAD: A Flexible Large-Scale AGV Routing System

2026-09-09

AI Summary

为解决大规模AGV车队路径规划问题，提出HiRAD框架，采用分层RL策略、连续空间表示和异步决策管道方法，减少行动空间，降低推理复杂度，缩短延迟。

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.

Problem

• Automatic Guided Vehicles
• Routing
• Reinforcement Learning
• Real-time Control

Innovation

• Hierarchical RL
• Continuous-space Routing
• Real-time Guarantees
• Asynchronous Event-driven Pipeline
• Spatiotemporal Representation