Trajectory prediction method for agricultural tracked robots based on slip parameter estimation

The trajectory prediction of tracked robots is the foundation and prerequisite for trajectory tracking and autonomous precise navigation. The kinematic model of the agricultural tracked robot considering the slips (slippages and slip-rotation) between the tracks and the soil is established by analyzing the slip and turning characteristics. The extended Kalman filter (EKF) method and the improved sliding mode observer (ISMO) method are respectively used to estimate the slip parameters of the agricultural tracked robot during the driving process. Subsequently, the driving trajectory of the agricultural tracked robot is predicted for a future time period, in combination with the provided control sequence. Finally, simulation and experimental results show that the proposed trajectory prediction method for agricultural tracked robots, which integrates slip parameter estimation, significantly reduces trajectory prediction errors. Moreover, the proposed ISMO method outperforms the traditional EKF method in terms of slip parameter estimation and driving trajectory prediction. The research in this paper provides theoretical guidance for trajectory planning and tracking control of agricultural tracked robots, and has broad application prospects.