Ahmadzadeh, SR, Leonetti, M orcid.org/0000-0002-3831-2400, Carrera, A et al. (3 more authors) (2014) Online Discovery of AUV Control Policies to Overcome Thruster Failures. In: 2014 IEEE International Conference on Robotics and Automation. ICRA, 31 May - 07 Jun 2014, Hong Kong. IEEE , pp. 6522-6528.
Abstract
We investigate methods to improve fault-tolerance of Autonomous Underwater Vehicles (AUVs) to increase their reliability and persistent autonomy. We propose a learning-based approach that is able to discover new control policies to overcome thruster failures as they happen. The proposed approach is a model-based direct policy search that learns on an on-board simulated model of the AUV. The model is adapted to a new condition when a fault is detected and isolated. Since the approach generates an optimal trajectory, the learned fault-tolerant policy is able to navigate the AUV towards a specified target with minimum cost. Finally, the learned policy is executed on the real robot in a closed-loop using the state feedback of the AUV. Unlike most existing methods which rely on the redundancy of thrusters, our approach is also applicable when the AUV becomes under-actuated in the presence of a fault. To validate the feasibility and efficiency of the presented approach, we evaluate it with three learning algorithms and three policy representations with increasing complexity. The proposed method is tested on a real AUV, Girona500.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2014 IEEE. This is an author produced version of a paper published in IEEE International Conference on Robotics and Automation. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Uploaded in accordance with the publisher's self-archiving policy. |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) > Artificial Intelligence & Biological Systems (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 01 Aug 2016 09:38 |
Last Modified: | 22 Jan 2018 15:48 |
Published Version: | http://dx.doi.org/10.1109/ICRA.2014.6907821 |
Status: | Published |
Publisher: | IEEE |
Identification Number: | 10.1109/ICRA.2014.6907821 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:101402 |