用学习+优化结合方法，提升千级机器人仓库调度效率
GRAND: Guidance, Rebalancing, and Assignment for Networked Dispatch in Multi-Agent Path Finding
- 用图神经网络生成全局调度指引，再分步优化分配
- 在500机器人场景下，吞吐量比2024冠军方案高10%
- 适合需要实时、大规模调度的智能仓储系统
大型机器人车队在仓库等物流场景中已成常态，微小控制改进即可带来显著运营效益。本文针对长期运行的多智能体取送任务（MAPD），提出一种混合调度方法：结合基于强化学习训练的图神经网络全局指引与轻量级优化。该网络输出空闲机器人在聚合仓库图上的期望分布，经最小费用流转化为区域间再平衡，并通过局部分配问题最终确定任务指派，在保持精度的同时将每步延迟控制在1秒内。该方法称为GRAND，即通过引导（Guidance）、再平衡（Rebalancing）与分配（Assignment）显式利用工作区网络结构。在含最多500个智能体的联盟机器人竞赛（LoRR）拥堵基准测试中，相比2024年冠军调度器，本方法提升吞吐量达10%，且满足实时执行要求。结果表明，将图结构学习指引与可解优化器结合，能有效缓解拥堵，为大规模机器人车队提供高效可扩展的调度范式。
原文摘要 · Abstract (English)
Large robot fleets are now common in warehouses and other logistics settings, where small control gains translate into large operational impacts. In this article, we address task scheduling for lifelong Multi-Agent Pickup-and-Delivery (MAPD) and propose a hybrid method that couples learning-based global guidance with lightweight optimization. A graph neural network policy trained via reinforcement learning outputs a desired distribution of free agents over an aggregated warehouse graph. This signal is converted into region-to-region rebalancing through a minimum-cost flow, and finalized by small, local assignment problems, preserving accuracy while keeping per-step latency within a 1 s compute budget. We call this approach GRAND: a hierarchical algorithm that relies on Guidance, Rebalancing, and Assignment to explicitly leverage the workspace Network structure and Dispatch agents to tasks. On congested warehouse benchmarks from the League of Robot Runners (LoRR) with up to 500 agents, our approach improves throughput by up to 10% over the 2024 winning scheduler while maintaining real-time execution. The results indicate that coupling graph-structured learned guidance with tractable solvers reduces congestion and yields a practical, scalable blueprint for high-throughput scheduling in large fleets.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。