With the rapid progress in Multi-Agent Path Finding (MAPF), researchers have studied how MAPF algorithms can be deployed to coordinate hundreds of robots in large automated warehouses. While most works try to improve the throughput of such warehouses by developing better MAPF algorithms, we focus on improving the throughput by optimizing the warehouse layout. We show that, even with state-of-the-art MAPF algorithms, commonly used human-designed layouts can lead to congestion for warehouses with large numbers of robots and thus have limited scalability. We extend existing automatic scenario generation methods to optimize warehouse layouts. Results show that our optimized warehouse layouts (1) reduce traffic congestion and thus improve throughput, (2) improve the scalability of the automated warehouses by doubling the number of robots in some cases, and (3) are capable of generating layouts with user-specified diversity measures. We include the source code at: https://github.com/lunjohnzhang/warehouse_env_gen_public

翻译：随着多智能体路径规划（MAPF）的快速发展，研究人员已探索如何部署MAPF算法来协调大型自动化仓库中数百个机器人的运作。尽管大多数工作通过开发更优的MAPF算法来提升此类仓库的吞吐量，但我们聚焦于通过优化仓库布局来提高吞吐量。我们证明，即使采用最先进的MAPF算法，对于配备大量机器人的仓库而言，常见的人为设计布局仍可能导致拥堵，从而限制了可扩展性。我们扩展了现有的自动场景生成方法，以优化仓库布局。结果表明，我们优化后的仓库布局（1）减少了交通拥堵，从而提高了吞吐量；（2）在某些情况下将机器人数量翻倍，从而提升了自动化仓库的可扩展性；（3）能够生成具有用户指定多样性指标的布局。源代码请见：https://github.com/lunjohnzhang/warehouse_env_gen_public