A Collaborative Control Model for Shop-Floor Material Handling System Based on Artificial Intelligence

Industrial robots like automated guided vehicles (AGV) have drawn increased attention due to their flexibility and low cost. However, there are still several challenges, such as serious waste of vehicle loading capacity and the inability to change strategies when needed. To address these challenges, an Artificial Intelligence (AI) based collaborative control model is proposed. To achieve rapid response to changes from both high-level control systems and plant environment, the optimized decision rules are built to support the AGV self-decision-making to improve flexibility and interoperability. Then, by sharing the information on transport tasks, the vehicle can submit requests to take tasks proactively. The production logistic service system is built to assess the transport capabilities of vehicles and optimize the task plans to decrease the No-load rate of vehicles, improving overall efficiency. The presented method is demonstrated by a set of simulations, which proved that the overall task completion efficiency and the utilization of the vehicles are improved.