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Online Direct Policy Search for Thruster Failure Recovery in Autonomous Underwater Vehicles

posted May 29, 2016, 2:33 PM by Reza Ahmadzadeh   [ updated Jun 26, 2017, 8:16 AM ]
Seyed Reza Ahmadzadeh, Matteo Leonetti, Petar Kormushev

Reference:
Seyed Reza Ahmadzadeh, Matteo Leonetti, Petar Kormushev, "Online Direct Policy Search for Thruster
Failure Recovery in Autonomous Underwater Vehicles", In Proc. 6th Intl Workshop on Evolutionary and
Reinforcement Learning for Autonomous Robot Systems (ERLARS 2013), 12th European Conf. on
Artificial Life (ECAL 2013), Taormina, Italy, 2-9 Sept. 2013.
Bibtex Entry:
@INPROCEEDINGS{ahmadzadeh2013online, TITLE={Online Direct Policy Search for Thruster Failure Recovery in Autonomous Underwater Vehicles}, AUTHOR={Ahmadzadeh, Seyed Reza and Leonetti, Matteo and Kormushev, Petar}, BOOKTITLE={6th International workshop on Evolutionary and Reinforcement Learning for Autonomous Robot System (ERLARS)}, YEAR={2013}, MONTH={September}, ADDRESS={Taormina, Italy} }
Abstract:
Autonomous underwater vehicles are prone to various factors that may lead a mission to fail and
cause unrecoverable damages. Even robust controllers cannot make sure that the robot is able to
navigate to a safe location in such situations. In this paper we propose an online learning method
for reconfiguring the controller, which tries to recover the robot and survive the mission using
the current asset of the system. The proposed method is framed in the reinforcement learning
setting, and in particular as a model-based direct policy search approach. Since learning on a
damaged vehicle would be impossible owing to time and energy constraints, learning is performed on
a model which is identified and kept updated online. We evaluate the applicability of our method
with different policy representations and learning algorithms, on the model of the Girona500
autonomous underwater vehicle.

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