Q-learning based Tracking Control and Slope Climbing Strategy Design of Autonomous Mobile Robot and Flatbed Vehicle

Thanks to advances in technology, the forth industrial revolution (Industry 4.0) is coming. All the manufacturers are undertaking large-scale technological innovations which include artificial intelligence and auto guided vehicle. These two research fields are also the major techniques in the proposed paper. In this paper, the tracking control and slope-climbing strategy between autonomous mobile robot and flatbed vehicle is proposed. The structure is integrated by three parts. First, the Q-learning algorithm is applied to controller design. Second, LIDAR sensor and camera are used to measure distance, forward direction and position of flatbed vehicle relative to autonomous mobile robot. Third, Robot Operation System (ROS) is adopt to be the data communication system among central processor unit, LIDAR sensor and camera of the autonomous mobile robot. In the simulation results, the flatbed vehicle follows three different trajectories, and the autonomous mobile robot computes tracking paths by machine vision and Q-learning algorithm. After reaching a certain distance, the autonomous mobile robot would carry out slope-climbing strategy to link with flatbed vehicle successfully.