Detection of unanticipated faults for autonomous underwater vehicles using online topic models
Date
2017-12-26Author
Raanan, Ben-Yair
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Bellingham, James G.
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Zhang, Yanwu
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Kemp, Mathieu
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Kieft, Brian
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Singh, Hanumant
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Girdhar, Yogesh
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https://hdl.handle.net/1912/10498As published
https://doi.org/10.1002/rob.21771DOI
10.1002/rob.21771Keyword
Autonomous underwater vehicle (AUV); Autonomy; Fault detection and diagnosis; Topic modelingAbstract
For 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.
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© 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.
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Journal of Field Robotics 35 (2018): 705-716The following license files are associated with this item:
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivatives 4.0 International
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