Dynamic Network Topology Analysis, Design, and Evaluation for Multi-Robot Vehicle Transfer in High-Density Storage Yards

Abstract

With the rapid advancement of intelligent manufacturing and the rise of emerging markets, global auto-mobile exports have surged, placing unprecedented demands on logistics infrastructure. Efficient coordination of multiple robots for vehicle autonomous transfer is essential in high-density storage environments. However, conventional navigation mode, where autonomous robots navigate the entire space, often leads to inefficiencies, congestion, and increased safety risks. To address these challenges, this paper proposes a dynamic network topology framework to optimize large-scale vehicle transfers in high-density environments. The approach models free space as a network graph with directional, weighted movement costs. Leveraging yard operational characteristics, real-time transfer conditions, and robot specific capabilities, we introduce an event-triggered mechanism to update the network topology dynamically. This method continuously refines drivable space, effectively integrating yard areas with roadways to enhance routing flexibility in robot scheduling. Scenario-Based evaluations demonstrate that the proposed approach reduces traveled distance by up to 12.3% and task completion time by 19.3% compared to traditional operational networks, leading to lower operational costs and improved task efficiency. Notably, these benefits become more pronounced as the number of robots increases and the operational environment grows more complex.