Reinforcement learning-based fuzzy controller for autonomous guided vehicle path tracking

Abstract

Automated guided vehicles (AGVs) play a critical role in connecting the entire production line. A fully automated AGV must perform four functions, namely simultaneous localization and mapping (SLAM), positioning, routing, and path tracking. In the present study, Hector SLAM, adaptive Monte Carlo localization, and Anytime Repairing A* were used to perform SLAM, localization, and path planning functions, respectively. For path tracking, a fuzzy proximal policy optimization (FPPO) controller was created by applying fuzzy control theory and incorporating reinforcement learning to improve the accuracy of the fuzzy controller’s output. Currently, extensive experience is required to manually design fuzzy rules and membership functions; an inappropriate design can lead to low control precision and poor dynamic system quality. The experimental results in both virtual and real environments demonstrated that the FPPO controller reduced both maximum and mean path tracking errors to a considerably greater extent than did a conventional fuzzy controller. In the virtual environment, the average tracking error for the circular trajectory decreased from 0.05 to 0.02 m, the U-shaped trajectory error decreased from 0.02 to 0.01 m, and the right-angle trajectory error decreased from 0.02 to 0.01 m, highlighting the FPPO controller’s high precision and stability. Similarly, in a real environment, the average tracking error for the circular trajectory decreased from 0.05 to 0.02 m, the U-shaped trajectory error decreased from 0.03 to 0.01 m, and the right-angle trajectory error decreased from 0.02 to 0.01 m. These results indicate that the FPPO controller exhibits exceptional adaptability and reliability across various path types. The FPPO controller overcomes this shortcoming by integrating reinforcement learning to optimize the fuzzy control; the method also provides a self-learning ability to the AGV. By comparison with a conventional fuzzy controller, the FPPO controller was demonstrated to improve the AGV’s path tracking ability.