Kieft Brian

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Kieft
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Brian
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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.
  • Article
    Autonomous four-dimensional mapping and tracking of a coastal upwelling front by an autonomous underwater vehicle
    (John Wiley & Sons, 2015-07-14) Zhang, Yanwu ; Bellingham, James G. ; Ryan, John P. ; Kieft, Brian ; Stanway, Michael J.
    Coastal upwelling is a wind-driven ocean process that brings cooler, saltier, and nutrient-rich deep water upward to the surface. The boundary between the upwelling water and the normally stratified water is called the “upwelling front.” Upwelling fronts support enriched phytoplankton and zooplankton populations, thus they have great influences on ocean ecosystems. Traditional ship-based methods for detecting and sampling ocean fronts are often laborious and very difficult, and long-term tracking of such dynamic features is practically impossible. In our prior work, we developed a method of using an autonomous underwater vehicle (AUV) to autonomously detect an upwelling front and track the front's movement on a fixed latitude, and we applied the method in scientific experiments. In this paper, we present an extension of the method. Each time the AUV crosses and detects the front, the vehicle makes a turn at an oblique angle to recross the front, thus zigzagging through the front to map the frontal zone. The AUV's zigzag tracks alternate in northward and southward sweeps, so as to track the front as it moves over time. This way, the AUV maps and tracks the front in four dimensions—vertical, cross-front, along-front, and time. From May 29 to June 4, 2013, the Tethys long-range AUV ran the algorithm to map and track an upwelling front in Monterey Bay, CA, over five and one-half days. The tracking revealed spatial and temporal variabilities of the upwelling front.
  • Article
    Targeted sampling by autonomous underwater vehicles
    (Frontiers Media, 2019-08-14) Zhang, Yanwu ; Ryan, John P. ; Kieft, Brian ; Hobson, Brett W. ; McEwen, Robert S. ; Godin, Michael A. ; Harvey, Julio B. ; Barone, Benedetto ; Bellingham, James G. ; Birch, James M. ; Scholin, Christopher A. ; Chavez, Francisco P.
    In the vast ocean, many ecologically important phenomena are temporally episodic, localized in space, and move according to local currents. To effectively study these complex and evolving phenomena, methods that enable autonomous platforms to detect and respond to targeted phenomena are required. Such capabilities allow for directed sensing and water sample acquisition in the most relevant and informative locations, as compared against static grid surveys. To meet this need, we have designed algorithms for autonomous underwater vehicles that detect oceanic features in real time and direct vehicle and sampling behaviors as dictated by research objectives. These methods have successfully been applied in a series of field programs to study a range of phenomena such as harmful algal blooms, coastal upwelling fronts, and microbial processes in open-ocean eddies. In this review we highlight these applications and discuss future directions.
  • Article
    Autonomous tracking of salinity-intrusion fronts by a long-range autonomous underwater vehicle
    (Institute of Electrical and Electronics Engineers, 2022-04-18) Zhang, Yanwu ; Yoder, Noa ; Kieft, Brian ; Kukulya, Amy L. ; Hobson, Brett W. ; Ryan, Svenja ; Gawarkiewicz, Glen G.
    Shoreward intrusions of anomalously salty water along the continental shelf of the Middle Atlantic Bight are often observed in spring and summer. Exchange of heat, nutrients, and carbon across the salinity-intrusion front has a significant impact on the marine ecosystem and fisheries. In this article, we developed a method of using an autonomous underwater vehicle (AUV) to detect a salinity-intrusion front and track the front's movement. Autonomous front detection is based on the different vertical structures of salinity in the two distinct water types: the vertical difference of salinity is large in the intruding saltier water because of the salinity “tongue” at mid-depth, but is small in the nearshore fresher water due to absence of the salinity anomaly. Every time the AUV crosses and detects the front, the vehicle makes a turn at an oblique angle to cross the front, thus zigzagging through the front to map the frontal zone. The AUV's zigzags sweep back and forth to track the front as it moves over time. From June 25 to 30, 2021, a Tethys-class long-range AUV mapped and tracked a salinity-intrusion front on the southern New England shelf. The frontal tracking revealed the salinity intrusion's 3-D structure and temporal evolution with unprecedented detail.