AGV Routing on Factory Floor by Reinforcement Learning

In the rapidly evolving landscape of industrial automation, Automated Guided Vehicles (AGVs) have become indispensable in enhancing efficiency and productivity in manufacturing and warehouse operations. This thesis project explores the optimization of routing and scheduling for AGVs in industrial environments using reinforcement learning (RL). Given the crucial role of AGVs in material handling and logistics within factories and warehouses, enhancing their efficiency is pivotal. The project is informed by the advancements in AI and the increasing integration of such technologies in industrial operations, aligning with initiatives like Sweden’s Production2030. A simulated environment where created and RL models where tested against traditional dispatched rules, underscoring the potential RL to enhance the productivity and efficiency of AGVs in warehouse settings.