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

Ariza Ramirez, W, Leong, ZQ ORCID: 0000-0002-0644-1822, Nguyen, HD ORCID: 0000-0003-0118-8597 and Jayasinghe, SG ORCID: 0000-0002-3304-9455 2020 , 'Exploration of the applicability of probabilistic inference for learning control in underactuated autonomous underwater vehicles' , Autonomous Robots, vol. 44 , 1121–1134 , doi: 10.1007/s10514-020-09922-z.

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Abstract

Underwater vehicles are employed in the exploration of dynamic environments where tuning of a specific controller for eachtask would be time-consuming and unreliable as the controller depends on calculated mathematical coefficients in idealisedconditions. For such a case, learning task from experience can be a useful alternative. This paper explores the capabilityof probabilistic inference learning to control autonomous underwater vehicles that can be used for different tasks withoutre-programming the controller. Probabilistic inference learning uses a Gaussian process model of the real vehicle to learn thecorrect policy with a small number of real field experiments. The use of probabilistic reinforcement learning looks for a simpleimplementation of controllers without the burden of coefficients calculation, controller tuning or system identification. A seriesof computational simulations were employed to test the applicability of model-based reinforcement learning in underwatervehicles. Three simulation scenarios were evaluated: waypoint tracking, depth control and 3D path tracking control. The 3Dpath tracking is done by coupling together a line-of-sight law with probabilistic inference for learning control. As a comparisonstudy LOS-PILCO algorithm can perform better than a robust LOS-PID. The results show that probabilistic model-basedreinforcement learning can be a deployable solution to motion control of underactuated AUVs as it can generate capablepolicies with minimum quantity of episodes.

Item Type: Article
Authors/Creators:Ariza Ramirez, W and Leong, ZQ and Nguyen, HD and Jayasinghe, SG
Keywords: machine learning, AUV, control, PILCO, LOS, underwater vehicle, path tracking, reinforcement learning
Journal or Publication Title: Autonomous Robots
Publisher: Springer New York LLC
ISSN: 0929-5593
DOI / ID Number: 10.1007/s10514-020-09922-z
Copyright Information:

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

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