Uncertainty Quantification of an Autonomous Surface Vehicle by Multi-fidelity Surrogate Models

Abstract

A multi-fidelity Gaussian process (MF-GP) is presented for the forward uncertainty quantification (UQ) of the performance of an autonomous surface vehicle (ASV) subject to uncertain operating conditions. The ASV is a shallow water autonomous multipurpose platform (SWAMP), designed for the acquisition of the environmental parameters in the extremely shallow waters of wetlands. The quantity of interest (QoI) is the hydrodynamic resistance of the SWAMP subject to variable payload and longitudinal position of its center of mass. The QoI is assessed by a linear potential-flow solver coupled with the rigid body equations of motion. Multiple fidelity levels are defined based on the computational grid size and the level of coupling between hydrodynamic loads and motions. The MF-GP is based on a low-fidelity surrogate, corrected with an additive function, representing the error between higher and lower fidelity solutions. The MF-GP provides the prediction with the associated uncertainty. The latter is used to adaptively train the MF-GP, adding points where the prediction uncertainty is maximum. Finally, the UQ of the QoI is performed by Monte Carlo sampling on the MF-GP surrogate. The first four statistical moments, the 95th percentile, and the probability density function of the QoI are assessed. MF-GP is compared to its single-fidelity (high-fidelity based) counterpart, showing overall better results.

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