Multi-Robot Coordination and Layout Design for Automated Warehousing


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

In Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI), August 19–25, 2023, Macao, China
Yulun Zhang
Yulun Zhang
Ph.D. Student

My research interests include Quality Diversity Optimization, Evolutionary Computation, Human-Robot Interaction, Multi-Robot Coordination, and Reinforcement Learning.