HiRAD: A Flexible Large-Scale AGV Routing System
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 ind...
| Authors | Yunjie Huang, Ruizhong Wu, Mengxuan Zhang et al. |
|---|---|
| Primary category | cs.RO |
| Published | 9 September 2026 |
| Last updated at source | 9 September 2026 |
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