  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.
