A mathematical study of the optimal path in an intelligent autonomous guided vehicles: A game theory approach

This paper proposes a new model for the optimal area coverage of machines in the production line by applying a single Autonomous Guided Vehicle (AGV) to minimize both transfer costs and the number of breakpoints of the AGV, as well as the equilibrium point. One of the unique advantages of the area coverage employed in the present study is that it minimizes transfer costs and breakpoints, making it possible to provide service for several machines simultaneously. The underlying assumption is to specify a path that ensures the coverage of every point in a given workspace at least once. Since a rail AGV is used in this study, the AGV can only traverse horizontal and vertical distances on the production line. The reversal of the AGV path in vertical and horizontal distances implies a kind of failure mode and a break of point in the present paper. The simulation results confirm the feasibility of the proposed method. Using game theory can help the system choose the most appropriate AGV to perform a task in a short time, thereby reducing the overall response time of the system and improving its efficiency. This paper employs a regulated speed policy to avoid conflicts, which can help maximize the efficiency of the system. It is demonstrated through simulation that the strategy improves the flexibility, robustness, and efficiency of the AGV system.