Safety-guaranteed trajectory planning and control based on GP estimation for unmanned surface vessels

TL;DR

This paper introduces a novel safety-guaranteed trajectory planning and control framework for unmanned surface vessels that leverages Gaussian processes to learn and manage uncertainties, ensuring safer navigation.

Contribution

It combines Gaussian process learning with Hamilton-Jacobi differential game-based planning to produce less conservative, safety-assured trajectories for USVs, validated through simulations and real-world experiments.

Findings

Successfully demonstrated in simulations and on a real USV

Achieved less conservative trajectories with safety guarantees

Validated effectiveness in real-world USV operations

Abstract

We propose a safety-guaranteed planning and control framework for unmanned surface vessels (USVs), using Gaussian processes (GPs) to learn uncertainties. The uncertainties encountered by USVs, including external disturbances and model mismatches, are potentially state-dependent, time-varying, and hard to capture with constant models. GP is a powerful learning-based tool that can be integrated with a model-based planning and control framework, which employs a Hamilton-Jacobi differential game formulation. Such a combination yields less conservative trajectories and safety-guaranteeing control strategies. We demonstrate the proposed framework in simulations and experiments on a CLEARPATH Heron USV.