Dynamic System Identification of Underwater Vehicles Using Multi-Output Gaussian Processes

TL;DR

This paper demonstrates that multi-output Gaussian processes effectively model the complex dynamics of underwater vehicles with limited data, outperforming recurrent neural networks and providing confidence measures.

Contribution

It introduces a novel application of multi-output Gaussian processes for AUV system identification, capturing inter-output relationships with minimal data.

Findings

Multi-output Gaussian processes outperform RNN in AUV dynamics modeling.

The method effectively models 6-DoF AUV with limited data.

Provides confidence measures alongside system identification.

Abstract

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.