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）技术的快速发展，研究者们已着手探索如何部署MAPF算法来协调大型自动化仓库中数百台机器人的运作。尽管大多数研究致力于通过开发更优的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效率。研究表明，即便采用最先进的MAPF算法，常用的人工设计布局仍会导致机器人数量较多的仓库出现拥堵，进而制约其可扩展性。我们扩展了现有的自动场景生成方法，用以优化仓库布局。实验结果表明，优化后的仓库布局能够：(1) 减少交通拥堵并提升吞吐量；(2) 在部分情况下使机器人数量翻倍，从而改善自动化仓库的可扩展性；(3) 生成具有用户指定多样性指标的布局。相关源代码已开源，访问地址：https://github.com/lunjohnzhang/warehouse_env_gen_public