Tracking ocean fronts with multiple vehicles and mixed communication losses

Reed, Brooks L.; Hover, Franz S.

doi:10.1109/iros.2013.6696837

Cited by 11 publications

(5 citation statements)

References 24 publications

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“…Researchers have made substantial progress in enabling one AUV to autonomously identify and track various oceanographic features, such as upwelling fronts, the depth of the thermocline, and phytoplankton patches (Cruz & Matos, ; Godin, Zhang, Ryan, Hoover, & Bellingham, ; Petillo, ; Petillo et al, ; Petillo, Balasuriya, & Schmidt, ; Zhang et al, ; Zhang, Godin, et al, ). Collaboration between multiple feature‐detecting AUVs would greatly enhance synopticity, coverage, and endurance of automated oceanographic studies (Petillo, ; Petillo et al, ; Reed & Hover, ). The complexity and low bandwidth of the underwater acoustic communications channel poses challenges to multi‐AUV collaboration; however, those challenges can be overcome (Kemna, Caron, & Sukhatme, ; Kemna, Rogers, Nieto‐Granda, Young, & Sukhatme, ; Schmidt, Benjamin, Petillo, & Lum, ).…”

Section: Comparison With Previous Workmentioning

confidence: 99%

Front delineation and tracking with multiple underwater vehicles

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Claus

et al. 2018

Journal of Field Robotics

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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. K E Y W O R D S adaptive sampling, autonomous underwater vehicles, multiasset planning, ocean front tracking 1 | INTRODUCTION Space-based remote sensing can provide extensive information about ocean dynamics. However, remote sensing information is generally limited to measuring the ocean surface. To probe the ocean interior efficiently requires marine vehicles such as autonomous underwater vehicles (AUVs), gliders, profiling buoys, surface vehicles, and ships sampling in situ. Unfortunately, building, deploying, and operating these in situ marine robotic explorers is expensive. As a result, any actual study involves a limited number of marine vehicles, especially when compared to the vast expanse of the ocean. Determining where to deploy and operate marine assets is a challenging problem given the 4D spatiotemporal variations in oceanographic phenomena.The use of autonomous marine vehicles will increase as the size of ocean observing systems expand to study the impact of the oceans on Earth's climate and ecosystems. The day-to-day operations of these systems will become increasingly difficult if human intervention is required. To enable large observing systems to operate more efficiently, techniques for autonomous control of assets based on science goals and data sources such as in situ J Field Robotics. 2019;36:568-586. wileyonlinelibrary.com/journal/rob

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Section: Comparison With Previous Workmentioning

confidence: 99%

Front delineation and tracking with multiple underwater vehicles

Branch

Flexas

Claus

et al. 2018

Journal of Field Robotics

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“…Other front tracking approaches described in related literature range from theoretical simulations with AUVs to determine variation of a front's position assuming a known environment [15], [16], [22], to distributing underwater gliders within the frontal boundary of a plume [23]. The simplicity of our approach-the zigzag motion and the tracking of an isotherm rather than a temperature gradient (which may dissipate or change from one stretch of the front to another)-keeps the complexity of this autonomous and adaptive front tracking method to a minimum, which is important for reducing the possible failure modes when deploying this technology in real, dynamic ocean environments.…”

Section: Novel Concepts and Approachmentioning

confidence: 99%

Autonomous & adaptive oceanographic front tracking on board autonomous underwater vehicles

Petillo

Schmidt

Lermusiaux

et al. 2015

OCEANS 2015 - Genova

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Abstract-Oceanic fronts, similar to atmospheric fronts, occur at the interface of two fluid (water) masses of varying characteristics. In regions such as these where there are quantifiable physical, chemical, or biological changes in the ocean environment, it is possible-with the proper instrumentation-to track, or map, the front boundary.In this paper, the front is approximated as an isotherm that is tracked autonomously and adaptively in 2D (horizontal) and 3D space by an autonomous underwater vehicle (AUV) running MOOS-IvP autonomy. The basic, 2D (constant depth) front tracking method developed in this work has three phases: detection, classification, and tracking, and results in the AUV tracing a zigzag path along and across the front. The 3D AUV front tracking method presented here results in a helical motion around a central axis that is aligned along the front in the horizontal plane, tracing a 3D path that resembles a slinky stretched out along the front.To test and evaluate these front tracking methods (implemented as autonomy behaviors), virtual experiments were conducted with simulated AUVs in a spatiotemporally dynamic MIT MSEAS ocean model environment of the Mid-Atlantic Bight region, where a distinct temperature front is present along the shelfbreak. A number of performance metrics were developed to evaluate the performance of the AUVs running these front tracking behaviors, and the results are presented herein.

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“…We extended the work of Imer et al [123] to the case of independent multi-channel packet losses [196 …”

Section: Constructive Techniquesmentioning

confidence: 99%

“…Simulations using three model datasets demonstrate the proof-of-concept. This chapter is based on work published in [196] and [197].…”

Section: Summary Of Contributionsmentioning

confidence: 99%

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Controller design for underwater vehicle systems with communication constraints

Reed¹

2015

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Real-time cooperation between autonomous vehicles can enable time-critical missions such as tracking and pursuit of a dynamic target or environmental feature, but relies on wireless communications. Underwater, communication over distances beyond about one hundred meters is almost exclusively accomplished through acoustics, which bring challenges such as propagation delays, low data rates, packet loss, and scheduling constraints due to interference and limited bandwidth. These limitations make underwater pursuit missions preeminent applications of networked control. Motivated by such applications, this thesis presents contributions towards multi-vehicle feedback control in the presence of severe communication constraints.The first major area of work considers the formulation and solution of new underwater multi-vehicle tracking and pursuit problems using closed-loop control. We begin with a centralized robust optimization approach for multicast routing and power control which is suitable for integration with vehicle control. Next, we describe field experiments in range-based target pursuit at high tracking bandwidths in a challenging shallow-water environment. Finally, we present a methodology for pursuit of dynamic ocean features such as fronts, which we validate using hindcast ocean model data. The primary innovation is a projection algorithm which carries out linearization of ocean model forecast dynamics and uncertainty directly in vehicle coordinates via a forward model technique. The resulting coupled linear stochastic system is suitable for networked control.The second area of work presents a unified formalism for multi-vehicle control and estimation with measurement, control, and acknowledgment packets all subject to scheduling, delays and packet loss. The modular framework we develop is built around a jump linear system description incorporating receding horizon optimization and buffering at actuators. Integration of these elements enables synthesis of a novel technique for estimation using delayed and lossy control acknowledgments-a desirable and practical capability of fielded systems that has not been considered to date.Simulations and field experiments demonstrate the effectiveness of our approach.

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Tracking ocean fronts with multiple vehicles and mixed communication losses

Cited by 11 publications

References 24 publications

Front delineation and tracking with multiple underwater vehicles

Front delineation and tracking with multiple underwater vehicles

Autonomous & adaptive oceanographic front tracking on board autonomous underwater vehicles

Controller design for underwater vehicle systems with communication constraints

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Tracking ocean fronts with multiple vehicles and mixed communication losses

Cited by 11 publications

References 24 publications

Front delineation and tracking with multiple underwater vehicles

Front delineation and tracking with multiple underwater vehicles

Autonomous &amp; adaptive oceanographic front tracking on board autonomous underwater vehicles

Controller design for underwater vehicle systems with communication constraints

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Autonomous & adaptive oceanographic front tracking on board autonomous underwater vehicles