Front delineation and tracking with multiple underwater vehicles. Issue 3 (12th December 2018)

• Record Type: Journal Article
• Authors: Branch, Andrew; Flexas, Mar M.; Claus, Brian; Thompson, Andrew F.; Zhang, Yanwu; Clark, Evan B.; Chien, Steve; Fratantoni, David M.; Kinsey, James C.; Hobson, Brett; Kieft, Brian; Chavez, Francisco P.
• Is Part Of: Journal of field robotics. Volume 36:Issue 3(2019)
• Page Start: 568
• Page End: 586
• Publication Date: 2018-12-12
• Subjects: adaptive sampling -- autonomous underwater vehicles -- multiasset planning -- ocean front tracking; Robots, Industrial -- Periodicals; Automatic control -- Periodicals; 629.892
• DOI: 10.1002/rob.21853
• Languages: English
• ISSNs: 1556-4959

Abstract

Abstract: This study describes a method for detecting and tracking ocean fronts using multiple autonomous underwater vehicles (AUVs). Multiple vehicles, equally spaced along the expected frontal boundary, complete near parallel transects orthogonal to the front. Two different techniques are used to determine the location of the front crossing from each individual vehicle transect. The first technique uses lateral gradients to detect when a change in the observed water property occurs. The second technique uses a measure of the vertical temperature structure over a single dive to detect when the vehicle is in upwelling water. Adaptive control of the vehicles ensure they remain perpendicular to the estimated front boundary as it evolves over time. This method was demonstrated in several experiment periods totaling weeks, in and around Monterey Bay, CA, in May and June of 2017. We compare the two front detection methods, a lateral gradient front detector and an upwelling front detector using the Vertical Temperature Homogeneity Index. We introduce two metrics to evaluate the adaptive control techniques presented. We show the capability of this method for repeated sampling across a dynamic ocean front using a fleet of three types of platforms: short‐range Iver AUVs, Tethys‐class long‐range AUVs, and Seagliders. This method extends to tracking gradients of different properties using a variety of vehicles.