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dc.contributor.authorRaanan, Ben-Yair  Concept link
dc.contributor.authorBellingham, James G.  Concept link
dc.contributor.authorZhang, Yanwu  Concept link
dc.contributor.authorKemp, Mathieu  Concept link
dc.contributor.authorKieft, Brian  Concept link
dc.contributor.authorSingh, Hanumant  Concept link
dc.contributor.authorGirdhar, Yogesh  Concept link
dc.date.accessioned2018-07-27T14:03:55Z
dc.date.available2018-07-27T14:03:55Z
dc.date.issued2017-12-26
dc.identifier.citationJournal of Field Robotics 35 (2018): 705-716en_US
dc.identifier.urihttps://hdl.handle.net/1912/10498
dc.description© The Author(s), 2017. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Journal of Field Robotics 35 (2018): 705-716, doi:10.1002/rob.21771.en_US
dc.description.abstractFor robots to succeed in complex missions, they must be reliable in the face of subsystem failures and environmental challenges. In this paper, we focus on autonomous underwater vehicle (AUV) autonomy as it pertains to self‐perception and health monitoring, and we argue that automatic classification of state‐sensor data represents an important enabling capability. We apply an online Bayesian nonparametric topic modeling technique to AUV sensor data in order to automatically characterize its performance patterns, then demonstrate how in combination with operator‐supplied semantic labels these patterns can be used for fault detection and diagnosis by means of a nearest‐neighbor classifier. The method is evaluated using data collected by the Monterey Bay Aquarium Research Institute's Tethys long‐range AUV in three separate field deployments. Our results show that the proposed method is able to accurately identify and characterize patterns that correspond to various states of the AUV, and classify faults at a high rate of correct detection with a very low false detection rate.en_US
dc.description.sponsorshipOffice of Naval Research Grant Number: N00014‐14‐1‐0199; David and Lucile Packard Foundationen_US
dc.language.isoen_USen_US
dc.publisherJohn Wiley & Sonsen_US
dc.relation.urihttps://doi.org/10.1002/rob.21771
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectAutonomous underwater vehicle (AUV)en_US
dc.subjectAutonomyen_US
dc.subjectFault detection and diagnosisen_US
dc.subjectTopic modelingen_US
dc.titleDetection of unanticipated faults for autonomous underwater vehicles using online topic modelsen_US
dc.typeArticleen_US
dc.identifier.doi10.1002/rob.21771


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Attribution-NonCommercial-NoDerivatives 4.0 International
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivatives 4.0 International