Model Predictive Control for Cooperative Docking Between Autonomous Surface Vehicles with Disturbance Rejection

TL;DR

This paper introduces a centralized Model Predictive Control method for cooperative USV docking that effectively rejects disturbances like water currents, enabling faster and more efficient docking compared to existing methods.

Contribution

It presents a novel cooperative docking approach using MPC that accounts for disturbances, improving upon traditional single-vehicle target methods.

Findings

Faster docking achieved in simulations.

Effective disturbance rejection demonstrated.

Enhanced constraint satisfaction during docking.

Abstract

Uncrewed Surface Vehicles (USVs) are a popular and efficient type of marine craft that find application in a large number of water-based tasks. When multiple USVs operate in the same area, they may be required to dock to each other to perform a shared task. Existing approaches for the docking between autonomous USVs generally consider one USV as a stationary target, while the second one is tasked to reach the required docking pose. In this work, we propose a cooperative approach for USV-USV docking, where two USVs work together to dock at an agreed location. We use a centralized Model Predictive Control (MPC) approach to solve the control problem, obtaining feasible trajectories that also guarantee constraint satisfaction. Owing to its model-based nature, this approach allows the rejection of disturbances, inclusive of exogenous inputs, by anticipating their effect on the USVs through the MPC prediction model. This is particularly effective in case of almost-stationary disturbances such as water currents. In simulations, we demonstrate how the proposed approach allows for a faster and more efficient docking with respect to existing approaches.