Multi-Sensor Wheel Slip Estimation and Compensated NMPC for Trajectory Tracking of Skid-Steered Mobile Robot

Abstract

During trajectory tracking in complex outdoor environments, skid-steered mobile robots are prone to slip disturbances arising from wheel-ground contact. This paper proposes a real-time wheel slip estimator and integrates it into the nonlinear model predict control (NMPC) framework. First, a wheel slip dynamics model is formulated and embedded it into an Extended Kalman Filter for slip estimation using multi-sensor data and high-fidelity sensor model. Then, an adaptive compensation mechanism is incorporated into the NMPC framework according to the estimated slip characteristics, thereby improving trajectory tracking performance. Finally, the real-world experiments are conducted to exhibit the effectiveness and robustness of the proposed method.