NDLI: Multi-objective reinforcement learning for AUV thruster failure recovery

Content Provider	IEEE Xplore Digital Library
Author	Ahmadzadeh, S.R. Kormushev, P. Caldwell, D.G.
Copyright Year	2014
Description	Author affiliation: Dept. of Adv. Robot., Ist. Italiano di Tecnol., Genoa, Italy (Ahmadzadeh, S.R.; Kormushev, P.; Caldwell, D.G.)
Abstract	This paper investigates learning approaches for discovering fault-tolerant control policies to overcome thruster failures in Autonomous Underwater Vehicles (AUV). The proposed approach is a model-based direct policy search that learns on an on-board simulated model of the vehicle. When a fault is detected and isolated the model of the AUV is reconfigured according to the new condition. To discover a set of optimal solutions a multi-objective reinforcement learning approach is employed which can deal with multiple conflicting objectives. Each optimal solution can be used to generate a trajectory that is able to navigate the AUV towards a specified target while satisfying multiple objectives. The discovered policies are executed on the robot in a closed-loop using AUV's state feedback. Unlike most existing methods which disregard the faulty thruster, our approach can also deal with partially broken thrusters to increase the persistent autonomy of the AUV. In addition, the proposed approach is applicable when the AUV either becomes under-actuated or remains redundant in the presence of a fault. We validate the proposed approach on the model of the Girona500 AUV.
Starting Page	1
Ending Page	8
File Size	666941
Page Count	8
File Format	PDF
ISBN	9781479945528
DOI	10.1109/ADPRL.2014.7010621
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-12-09
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Sociology Vectors Trajectory Statistics Vehicle dynamics Optimization Vehicles
Content Type	Text
Resource Type	Article

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