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

Post date: May 29, 2016 5:15:56 PM

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.