Publications‎ > ‎Conference Papers‎ > ‎

On-line Learning to Recover from Thruster Failures on Autonomous Underwater Vehicles

posted May 29, 2016, 10:15 AM by Reza A   [ updated Jun 26, 2017, 8:28 AM ]
Matteo Leonetti, Seyed Reza Ahmadzadeh, Petar Kormushev

Reference:
Matteo Leonetti, Seyed Reza Ahmadzadeh, Petar Kormushev, "On-line Learning to Recover from Thruster
Failures on Autonomous Underwater Vehicles", In Proc. MTS/IEEE Intl Conf. OCEANS 2013, San Diego,
CA, USA, 23-26 Sept. 2013.
Bibtex Entry:
@INPROCEEDINGS{leonetti2013line, TITLE={On-line Learning to Recover from Thruster Failures on Autonomous Underwater Vehicles}, AUTHOR={Leonetti, Matteo and Ahmadzadeh, Seyed Reza and Kormushev, Petar}, BOOKTITLE={{MTS/IEEE OCEANS} }, YEAR={2013}, MONTH={September}, ORGANIZATION={IEEE}, PAGES={1--6}, ADDRESS={San Diego, USA} }
Abstract:
We propose a method for computing on-line the controller of an Autonomous Underwater Vehicle under
thruster failures. The method is general and can be applied to both redundant and under-actuated
AUVs, as it does not rely on the modification of the thruster control matrix. We define an
optimization problem on a specific class of functions, in order to compute the optimal control law
that achieves the target without using the faulty thruster. The method is framed within model-based
policy search for reinforcement learning, and we study its applicability on the model of the AUV
Girona500. We performed experiments with policies of increasing complexity, testing the on-line
feasibility of the approach as the optimization problem becomes more complex.

PDF Preview: