Slip-Compensation-Based Path Tracking Control for Tracked Robots Using VBEKF and Backstepping Control

Abstract

Tracked robots are widely used in agriculture, military, mining, and other fields. During the traveling process of tracked robots, the complex interaction between the tracks and the ground causes slip, which leads to problems such as low path tracking accuracy and poor control stability. To solve this thorny problem, quantitative control compensation parameters are generally obtained through experiments, or extended Kalman filter (EKF) is designed based on Gaussian noise to estimate slip parameters during motion. However, the sensor measurement noise of tracked robots usually exhibits a non-Gaussian distribution in uneven terrain and variable soil conditions. Under such circumstances, these methods exhibit larger errors and demonstrate inadequate adaptability. Therefore, this paper proposes a variational Bayesian EKF (VBEKF) algorithm for slip parameters estimation, and designs a slip compensation path tracking controller to improve the accuracy and adaptability of the control system of tracked robots under complex operating conditions. The main contributions of this paper are as follows: (1) The non-Gaussian noise was re-modeled using the Student's t-distribution, and combined with variational Bayesian, the VBEKF algorithm was designed. This algorithm can more accurately estimate the slip parameters between the tracks and soil under complex and varying operating conditions, demonstrating enhanced adaptability. (2) Based on the backstepping control principle, a path tracking controller with slip parameter compensation was designed for tracked robots. This controller dynamically adjusts its output control based on the estimated slip parameters to eliminate the impact of slip between the tracks and soil on path tracking accuracy. Finally, the effectiveness of the method was demonstrated through simulations and experiments. This study can improve the adaptability and stability of tracked robots under complex and variable operating conditions, ensuring accurate and rapid task completion, and has broad application prospects.