Artificial intelligence empowered multi-AGVs in manufacturing systems
This work addresses efficiency and deadlock issues for AGV systems in manufacturing, presenting an incremental improvement over existing methods.
The paper tackled the problem of improving efficiency and preventing deadlocks in multi-AGV manufacturing systems by proposing an approach that combines a traditional scheduling algorithm with an artificial neural network to predict future tasks and pre-position AGVs, resulting in efficiency improvements of 20% to 30% compared to traditional methods.
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%.