Raanan Ben-Yair

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Raanan
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Ben-Yair
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  • Article
    Autonomous tracking and sampling of the deep chlorophyll maximum layer in an open-ocean eddy by a long-range autonomous underwater vehicle
    (Institute of Electrical and Electronics Engineers, 2020-10-13) Zhang, Yanwu ; Kieft, Brian ; Hobson, Brett W. ; Ryan, John P. ; Barone, Benedetto ; Preston, Christina M. ; Roman, Brent ; Raanan, Ben-Yair ; Marin, Roman ; O’Reilly, Thomas C. ; Rueda, Carlos A. ; Pargett, Douglas ; Yamahara, Kevan M. ; Poulos, Steve ; Romano, Anna ; Foreman, Gabe ; Ramm, Hans ; Wilson, Samuel T. ; DeLong, Edward F. ; Karl, David M. ; Birch, James M. ; Bellingham, James G. ; Scholin, Christopher A.
    Phytoplankton communities residing in the open ocean, the largest habitat on Earth, play a key role in global primary production. Through their influence on nutrient supply to the euphotic zone, open-ocean eddies impact the magnitude of primary production and its spatial and temporal distributions. It is important to gain a deeper understanding of the microbial ecology of marine ecosystems under the influence of eddy physics with the aid of advanced technologies. In March and April 2018, we deployed autonomous underwater and surface vehicles in a cyclonic eddy in the North Pacific Subtropical Gyre to investigate the variability of the microbial community in the deep chlorophyll maximum (DCM) layer. One long-range autonomous underwater vehicle (LRAUV) carrying a third-generation Environmental Sample Processor (3G-ESP) autonomously tracked and sampled the DCM layer for four days without surfacing. The sampling LRAUV's vertical position in the DCM layer was maintained by locking onto the isotherm corresponding to the chlorophyll peak. The vehicle ran on tight circles while drifting with the eddy current. This mode of operation enabled a quasi-Lagrangian time series focused on sampling the temporal variation of the DCM population. A companion LRAUV surveyed a cylindrical volume around the sampling LRAUV to monitor spatial and temporal variation in contextual water column properties. The simultaneous sampling and mapping enabled observation of DCM microbial community in its natural frame of reference.
  • Article
    Detection of unanticipated faults for autonomous underwater vehicles using online topic models
    (John Wiley & Sons, 2017-12-26) Raanan, Ben-Yair ; Bellingham, James G. ; Zhang, Yanwu ; Kemp, Mathieu ; Kieft, Brian ; Singh, Hanumant ; Girdhar, Yogesh
    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.