Poster
in
Workshop: Safe and Robust Control of Uncertain Systems
Safety-guaranteed trajectory planning and control based on GP estimation for unmanned surface vessels
Shuhao Zhang · Seth Siriya
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 combination yields less conservative trajectories and safety-guaranteeing control strategies. We demonstrate the proposed framework in simulations and experiments on a CLEARPATH Heron USV.