Path Tracking Algorithm for Mobile Robot Based on Learning-based Nonlinear Model Predictive Control

As a key vehicle in disaster relief scenarios, the reliability and path-following capability of autonomous vehicles are crucial. We developed a tracked mobile robot with a robust chassis to adapt to varied road conditions. A machine learning-based nonlinear model predictive control (NMPC) algorithm, combining the XGBoost model with insights gained from the disturbances encountered during the mobile robot's operation, significantly reduces path-tracking errors. Experimental results demonstrate an average path-following error of 14 cm on smooth surfaces and 17 cm on surfaces with potholes and slopes, showcasing excellent performance across different road conditions. With accumulated experience in repeated path following, further performance improvements are achievable.