Algorithm reduces robot congestion in human-friendly warehouses
CARVS: A Congestion-Aware Algorithm for Online Multi-Agent Pickup-and-Delivery in Human-Collaborative Warehouses
Multiagent SystemsArtificial IntelligenceRobotics
Summary
Robots working alongside people in warehouses can cause traffic jams and delays because people act unpredictably. The authors study this problem by adding human factors to a classic robot task scheduling challenge. They propose an algorithm called CARVS that smartly plans robot routes and swaps tasks to avoid congestion caused by humans and robots interacting. Their tests show CARVS handles bigger and busier scenarios better than older methods, reducing delays and keeping things running smoothly.
What this means in practice
- •For warehouse automation teams: Improve multi-robot coordination algorithms to reduce traffic jams caused by human-robot interactions in busy warehouses.
- •For logistics software developers: Incorporate congestion-aware task swapping to enhance real-time robot task assignment in human-integrated delivery environments.
Authors
Chaoran Li, Zeyuan Zhao, Shao Zhang
Abstract
Intelligent warehouse systems have transformed logistics by integrating robots with minimal infrastructure changes, boosting efficiency and optimizing workflows compared to traditional human-only operations. However, human-robot collaboration introduces new challenges, particularly due to human uncertainty, which leads to dynamic interactions and unpredictable behavior. To address these complexities, this paper presents the Multi-Agent Pickup-and-Delivery with Human Collaboration (MAPDwHC) problem, an extension of the Multi-Agent Pickup-and-Delivery (MAPD) problem that incorporates human unpredictability. This problem adds key constraints, such as dynamic obstacles caused by robots temporarily blocking paths when interacting with humans, and the need for real-time responsiveness. We formalize the MAPDwHC problem with additional constraints and properties. Based on this problem setting, we propose the CARVS algorithm, which improves task assignment and path planning by integrating task swapping, validation checks, and random initialization. CARVS reduces congestion, enhances adaptability to human-robot interactions, and ensures real-time responsiveness. Simulation results show that CARVS significantly reduces timeouts of path planning in large-scale scenarios where traditional MAPD algorithms fail, improving both efficiency and scalability.