Research on the straight-line driving method of Ackermann unmanned vehicle on rugged terrain based on differential drive constraints and integral prediction

A deviation prediction and control method based on a differential kinematic model is proposed for Ackermann-steered autonomous vehicles. In complex terrains, factors such as surface irregularities and friction frequently cause deviations between the vehicle’s driving direction and its longitudinal axis. Forward and inverse kinematic models are constructed to describe the relationship between the driving wheel speed and the front wheel steering angle under known motion states, as well as to analyze the vehicle’s motion behavior under specified control inputs. To mitigate heading angle errors and position deviations induced by uneven terrain, an active disturbance-resistant differential driving constraint method is introduced. This method dynamically regulates the travel distances of the left and right wheels through encoder feedback to maintain straight-line motion. Furthermore, based on the current position and heading deviations, an integral prediction model is employed to forecast the deviation trend at the next time step. Vehicle speed and steering angle are then adaptively adjusted by integrating this prediction with predefined safety parameters. Simulation results indicate that the proposed method effectively reduces path-following errors and enhances the accuracy and stability of autonomous vehicle control in complex terrains. This study provides both a theoretical basis and practical reference for path tracking control in unstructured environments.