A Slip Parameter Prediction Method Based on a Fusion Framework of Nonlinear Observer and Machine Learning

Slip parameter prediction is crucial for motion planning and control of unmanned skid-steering vehicles in off-road environments. Slip parameter prediction methods based on nonlinear observers and those based on machine learning models both have limitations under various conditions. Therefore, this paper presents a multi-layer Adaptive Unscented Kalman Filter (AUKF) slip parameter prediction method based on a fusion framework of nonlinear observers and machine learning models. The method first constructs the 0-layer AUKF using the vehicle kinematic model and sensor data to initialize the slip parameters. Then, with the input of the desired sequences of wheel speeds generated by the autonomous driving system, the 1-layer AUKF is constructed by combining the machine learning predictive model and running N times to obtain the future slip parameter sequence. Experimental data was collected by driving on paved and dirt roads with a skid-steering vehicle. The experimental results show that the method in this paper outperforms methods based on nonlinear observers in terms of slip parameter prediction accuracy when the prediction time domain is long. Furthermore, when faced with unknown conditions, this method shows superior robustness compared to methods based on machine learning models.