Congestion-Aware Multi-Agent Path Planning for Pick-Up and Delivery Tasks

Mobile robotic systems play a pivotal role in logistics, particularly in warehouse operations, where efficient and collision-free navigation is essential for completing tasks. However, managing a large number of robots often leads to congestion, causing delays and adversely affecting system scalability. This paper proposes a novel online algorithm for solving the Multi-Agent Pickup and Delivery (MAPD) problem using a decoupled approach. The algorithm addresses local collision detection and global congestion avoidance by integrating a congestion prediction model to enhance process efficiency. A deep learning framework is employed to approximate congestion predictions independently of the number of agents, reducing computational complexity. Simulation experiments demonstrate that the proposed approach significantly improves system throughput and scalability, with a notable average doubling of throughput in specific scenarios. The findings provide a foundation for advanced congestion management strategies in multi-agent systems, paving the way for efficient and scalable deployment in logistics and beyond.