An Improved Adaptive Filter Trajectory Tracking Algorithm Based on A Single Steering Wheel AGV

Abstract

Trajectory tracking is one of the key technologies to achieve mobile automated guided vehicle (AGV) and a necessary condition for the stable operation of AGV. At present, the most common AGV structure in the field of heavy load handling is the single steering wheel structure, most of the existing trajectory tracking algorithms are very complex and difficult to apply in practice. In this paper, the kinematic model is constructed by analyzing the kinematic state and structural characteristics of a single steering wheel AGV. The adaptive Kalman filtering algorithm is used to establish the equation of state with the position information of the AGV at the last moment and the three parameters set, then the observation equation is established with the position returned by the navigation sensor, and the new information obtained during the movement of the AGV is used to iteratively calculate the control law that makes the system asymptotically stable. The idea of a sliding data window was added to the controller, taking into account the effect of the ground flatness and body assembly tolerances on the control effect during the actual movement. The results show that the algorithm is simple and effective, and meets the requirements of AGV for trajectory tracking, and achieves the ideal application results.