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

TL;DR

This paper introduces a data-driven method for autonomous port maneuvers of USVs using Gaussian mixture models and a robust control approach, effectively handling nonlinear dynamics and environmental disturbances.

Contribution

It presents a novel learning and control framework combining Gaussian mixture models with Sontag's formula for USV port maneuvers, ensuring convergence and robustness.

Findings

Successfully learned complex port trajectories from real data

Demonstrated robustness of control under wind and currents

Analyzed computational complexity of the approach

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.