Task-level Collaborative Ad-hoc Autonomous Guided Vehicles (AGV) for Efficient Warehouse Logistics

This paper investigates task-level collaboration among Automated Guided Vehicles (AGVs) to enhance efficiency in warehouse logistics. Traditional AGV systems primarily focus on individual task completion, with limited collaboration restricted to crash avoidance and path optimization. This study explores a novel approach, where AGVs dynamically divide and coordinate subtasks to optimize collective performance. Using experimental setups, we evaluate the performance of single-robot and collaborative modes across two distinct path environments. Results demonstrate that task-level collaboration reduces idle time and improves throughput in certain scenarios, particularly as task complexity and path length increase. It identifies challenges such as inefficiencies during freight handovers and system scalability with the used hardware.