  Non-parametric system identification with Gaussian Processes for underwater
vehicles is explored in this research with the purpose of modelling autonomous
underwater vehicle (AUV) dynamics with low amount of data. Multi-output
Gaussian processes and its aptitude to model the dynamic system of an
underactuated AUV without losing the relationships between tied outputs is
used. The simulation of a first-principles model of a Remus 100 AUV is employed
to capture data for the training and validation of the multi-output Gaussian
processes. The metric and required procedure to carry out multi-output Gaussian
processes for AUV with 6 degrees of freedom (DoF) is also shown in this paper.
Multi-output Gaussian processes are compared with the popular technique of
recurrent neural network show that Multi-output Gaussian processes manage to
surpass RNN for non-parametric dynamic system identification in underwater
vehicles with highly coupled DoF with the added benefit of providing a
measurement of confidence.
