  AGVs are driverless robotic vehicles that picks up and delivers materials.
How to improve the efficiency while preventing deadlocks is the core issue in
designing AGV systems. In this paper, we propose an approach to tackle this
problem.The proposed approach includes a traditional AGV scheduling algorithm,
which aims at solving deadlock problems, and an artificial neural network based
component, which predict future tasks of the AGV system, and make decisions on
whether to send an AGV to the predicted starting location of the upcoming
task,so as to save the time of waiting for an AGV to go to there first when the
upcoming task is created. Simulation results show that the proposed method
significantly improves the efficiency as against traditional method, up to 20%
to 30%.
