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

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Title: Online Direct Policy Search for Thruster Failure Recovery in Autonomous Underwater Vehicles
Authors: Ahmadzadeh, SR
Leonetti, M
Kormushev, P
Item Type: Conference Paper
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 vehicle Girona500.
Issue Date: 2-Sep-2013
Date of Acceptance: 2-Sep-2013
Journal / Book Title: 6th International workshop on Evolutionary and Reinforcement Learning for Autonomous Robot System (ERLARS 2013), in conjunction with the 12th European Conference on Artificial Life (ECAL 2013)
Copyright Statement: © 2013 The Authors
Conference Name: ECAL 2013 12th European Conference on Artificial Life
Publication Status: Published
Start Date: 2013-09-02
Finish Date: 2013-09-06
Conference Place: Taormina, Italy
Appears in Collections:Dyson School of Design Engineering

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