Interaction-Minimizing Roadmap Optimization for High-Density Multi-Agent Path Finding

Modern industry increasingly demands customizability from each element of their workflow and factories. A prominent example for this is the advent of Automated Guided Vehicles (AGV) in intralogistics tasks, which autonomously navigate the manufacturing floor, reacting dynamically to variations in the workflow. One such application makes use of magnetically propelled planar drive systems to transport products between manufacturing stations, replacing traditional solutions which are limited in their ability to efficiently adapt to new requirements.This work presents an AGV control approach capable of offloading large parts of the computational expense into an offline preprocessing step: A unidirectional roadmap is generated using alternating position optimization and network modification operations, with the goal of reducing the number of interactions between agents to be resolved at runtime. This concept was successfully validated in simulation. An accompanying tech report and implementation further details the presented approach, as well as the used controller and simulator.