Data-Driven Model Predictive Control for Skid-Steering Unmanned Ground Vehicles

Skid steering vehicles rely on tracks slipping to perform turning maneuvers. In this context, the estimation of the right amount of slip turns out to be significant to correctly perform precise movements. In a typical agricultural scenario, with rough terrain and narrow navigating spaces, a reliable slip estimation is crucial to perform safe motions. In this work, we propose a novel Gaussian Process approach to slip estimation in a tracked wheel robots by showing experimental results obtained from our prototype robotic platform.