University Of Tasmania

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Exploration of the applicability of probabilistic inference for learning control in underactuated autonomous underwater vehicles

journal contribution
posted on 2023-05-21, 00:56 authored by Ariza Ramirez, W, Zhi Quan LeongZhi Quan Leong, Hung NguyenHung Nguyen, Shantha Jayasinghe ArachchillageShantha Jayasinghe Arachchillage
Underwater vehicles are employed in the exploration of dynamic environments where tuning of a specific controller for each task would be time-consuming and unreliable as the controller depends on calculated mathematical coefficients in idealised conditions. For such a case, learning task from experience can be a useful alternative. This paper explores the capability of probabilistic inference learning to control autonomous underwater vehicles that can be used for different tasks without re-programming the controller. Probabilistic inference learning uses a Gaussian process model of the real vehicle to learn the correct policy with a small number of real field experiments. The use of probabilistic reinforcement learning looks for a simple implementation of controllers without the burden of coefficients calculation, controller tuning or system identification. A series of computational simulations were employed to test the applicability of model-based reinforcement learning in underwater vehicles. Three simulation scenarios were evaluated: waypoint tracking, depth control and 3D path tracking control. The 3D path tracking is done by coupling together a line-of-sight law with probabilistic inference for learning control. As a comparison study LOS-PILCO algorithm can perform better than a robust LOS-PID. The results show that probabilistic model-based reinforcement learning can be a deployable solution to motion control of underactuated AUVs as it can generate capable policies with minimum quantity of episodes.


Publication title

Autonomous Robots








Australian Maritime College


Springer New York LLC

Place of publication

Van Godewijckstraat 30, Dordrecht, Netherlands, 3311 Gz

Rights statement

© Springer Science+Business Media, LLC, part of Springer Nature 2020

Repository Status

  • Restricted

Socio-economic Objectives

Autonomous water vehicles; Expanding knowledge in engineering