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


Ariza Ramirez, W and Leong, ZQ and Nguyen, HD and Jayasinghe, SG, Exploration of the applicability of probabilistic inference for learning control in underactuated autonomous underwater vehicles, Autonomous Robots, 44 pp. 1121-1134. ISSN 0929-5593 (2020) [Refereed Article]

Copyright Statement

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

DOI: doi:10.1007/s10514-020-09922-z


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.

Item Details

Item Type:Refereed Article
Keywords:machine learning, AUV, control, PILCO, LOS, underwater vehicle, path tracking, reinforcement learning
Research Division:Engineering
Research Group:Control engineering, mechatronics and robotics
Research Field:Simulation, modelling, and programming of mechatronics systems
Objective Division:Transport
Objective Group:Water transport
Objective Field:Autonomous water vehicles
UTAS Author:Ariza Ramirez, W (Mr Wilmer Ariza Ramirez)
UTAS Author:Leong, ZQ (Dr Zhi Leong)
UTAS Author:Nguyen, HD (Dr Hung Nguyen)
UTAS Author:Jayasinghe, SG (Dr Shantha Jayasinghe Arachchillage)
ID Code:145437
Year Published:2020
Web of Science® Times Cited:6
Deposited By:NC Maritime Engineering and Hydrodynamics
Deposited On:2021-07-21
Last Modified:2021-09-29

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