Multi-Robot Coordination and Layout Design for Automated Warehousing

Abstract

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

Accepted to International Joint Conference on Artificial Intelligence (IJCAI), 2023. The paper can be found at IJCAI 2023 proceeding at https://www.ijcai.org/proceedings/2023/0611

References in corpus (3)

• Robots that can adapt like animals
• Illuminating search spaces by mapping elites
• Integrated Task Assignment and Path Planning for Capacitated Multi-Agent Pickup and Delivery

Cited by in corpus (4)

• Learning Team-Based Navigation: A Review of Deep Reinforcement Learning Techniques for Multi-Agent Pathfinding
• Scaling Lifelong Multi-Agent Path Finding to More Realistic Settings: Research Challenges and Opportunities
• Workspace Optimization Techniques to Improve Prediction of Human Motion During Human-Robot Collaboration
• Destination-to-Chutes Task Mapping Optimization for Multi-Robot Coordination in Robotic Sorting Systems