Learning port maneuvers from data for automatic guidance of Unmanned Surface Vehicles

• The expertise piloting the USV is learned using Gaussian models. • Environmental disturbances are rejected by a controller based on Sontag’s Formula. • Methodology for the automatic guidance of the USV with stability guarantees. • Real dataset coming from Vendaval USV piloted on Ceuta port, Spain. At shipping ports, some repetitive maneuvering tasks such as entering/leaving port, transporting goods inside it or just making surveillance activities, can be efficiently and quickly carried out by a domestic pilot according to his/her experience. This know-how can be seized by Unmanned Surface Vehicles (USV) in order to autonomously replicate the same tasks. However, the inherent nonlinearity of ship trajectories and environmental perturbations as wind or marine currents make it difficult to learn a model and its respective control. We therefore present a data-driven learning and control methodology for USV, which is based on Gaussian Mixture Model, Gaussian Mixture Regression and the Sontag’s universal formula. Our approach is capable to learn the nonlinear dynamics as well as guarantee the convergence toward the target with a robust controller. Real data have been collected through experiments with a vessel at the port of Ceuta. The complex trajectories followed by an expert have been learned including the robust controller. The effect of the controller over noise/perturbations are presented, a measure of error is used to compare estimates and real data trajectories, and finally, an analysis of computational complexity is performed.