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

TL;DR

This work tackles autonomous port docking for USVs under environmental disturbances by learning from expert demonstrations. It fuses Gaussian Mixture Models and Gaussian Mixture Regression to capture nonlinear port trajectories and uses a CLF-based control law via a Sontag-inspired formula to guarantee convergence to the target. The approach is evaluated on the Vendaval USV in the Ceuta port, showing robust disturbance rejection and the ability to learn both position and heading with manageable computational cost. The results indicate practical potential for data-driven guidance in constrained, real-world maritime environments, with future extensions toward dispersion handling and obstacle integration.

Abstract

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 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.

Figures (13)

• Figure 1: USV Vendaval.
• Figure 2: Vendaval USV functional architecture.
• Figure 3: Map of the zone.
• Figure 4: Trajectories at the zone.
• Figure 5: Adjustment of three Gaussian kernels for a 1D system