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Online Discovery of AUV Control Policies to Overcome Thruster Failures

posted May 30, 2016, 3:08 PM by Reza A   [ updated Jun 24, 2017, 10:20 AM ]
Seyed Reza Ahmadzadeh, Matteo Leonetti, Arnau Carrera, Marc Carreras, Petar Kormushev, Darwin G. Caldwell

Reference:
Seyed Reza Ahmadzadeh, Matteo Leonetti, Arnau Carrera, Marc Carreras, Petar Kormushev, Darwin G.
Caldwell, "Online Discovery of AUV Control Policies to Overcome Thruster Failures", In Proc. IEEE
Intl Conf. on Robotics and Automation, (ICRA 2014), Hong Kong, China, pp. 6522-6528, 31 May-7 June
2014.
Bibtex Entry:
@INPROCEEDINGS{ahmadzadeh2014online, TITLE={Online Discovery of {AUV} Control Policies to Overcome Thruster Failure}, AUTHOR={Ahmadzadeh, Seyed Reza and Leonetti, Matteo and Carrera, Arnau and Carreras, Marc and Kormushev, Petar and Caldwell, Darwin G.}, BOOKTITLE={Robotics and Automation ({ICRA}), {IEEE} International Conference on}, PAGES={6522--6528}, YEAR={2014}, MONTH={May}, ADDRESS={Hong Kong, China}, ORGANIZATION={IEEE}, DOI={10.1109/ICRA.2014.6907821} }
DOI:
10.1109/ICRA.2014.6907821
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.

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