Autonomous tracking of a horizontal boundary

Cruz, Nuno; Matos, Aníbal

doi:10.1109/oceans.2014.7003275

Cited by 4 publications

(4 citation statements)

References 16 publications

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“…An important aspect for all front tracking to keep contact with the boundary feature. In [24] this is addressed by adapting the orientation of the crossings to the local curvature of the boundary.…”

Section: Two-dimensional [2d]mentioning

confidence: 99%

Adaptive Sampling of Surface Fronts in the Arctic Using an Autonomous Underwater Vehicle

Fossum

Norgren

Fer

et al. 2021

IEEE J. Oceanic Eng.

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Fronts between Arctic-and Atlantic-origin waters are characterized by strong lateral gradients in temperature and salinity. Ocean processes associated with fronts are complex with considerable space and time variability. Resolving the processes in frontal zones by observation is therefore challenging but important for understanding the associated physical-biological interactions and their impact on the marine ecosystem. The use of autonomous robotic vehicles and in situ data-driven sampling can help improve and augment the traditional sampling practices such as ships and profiling instruments. Here we present the development and results of using an autonomous agent for detection and sampling of an Arctic front, integrated on board an autonomous underwater vehicle. The agent is based on a subsumption architecture implemented as behaviors in a finite state machine. Once a front is detected, the front tracking behavior uses observations to continuously adapt the path of the vehicle to perform transects across the front interface. Following successful sea trials in the Trondheimsfjord, the front tracking agent was deployed to perform a full-scale mission near 82°N north of Svalbard, close to the

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Section: Two-dimensional [2d]mentioning

confidence: 99%

Adaptive Sampling of Surface Fronts in the Arctic Using an Autonomous Underwater Vehicle

Fossum

Norgren

Fer

et al. 2021

IEEE J. Oceanic Eng.

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“…The front is locally defined by an on‐board linear estimation, based on a single AUV's previous front crossing locations within a synoptic range. Cruz and Matos () track any gradient boundary using a single vehicle following a dynamic zigzag pattern and a lateral gradient detection algorithm to estimate the gradient boundary using an arc whose curvature is defined by the last three front‐crossing locations. Although significant work has been completed in simulation, very little has been performed in the field.…”

Section: Contextmentioning

confidence: 99%

“…Zhang et al (), () utilize the VTHI front detection method on a single vehicle to detect and track an upwelling front on a zigzag track with a fixed turn angle. Cruz and Matos () track any gradient boundary using a single vehicle following a dynamic zigzag pattern and a lateral gradient detection algorithm to estimate the gradient boundary using an arc whose curvature is defined by the last three front‐crossing locations. Petillo () and Petillo et al () use single and multiple autonomously coordinated AUVs following a dynamic zigzag across a threshold value of a property (e.g., temperature) that was determined from the property's peak lateral gradient (front) location.…”

Section: Comparison With Previous Workmentioning

confidence: 99%

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Front delineation and tracking with multiple underwater vehicles

Branch

Flexas

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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Autonomous tracking of an oceanic thermal front by a Wave Glider

Zhang

Rueda

Kieft

et al. 2019

Journal of Field Robotics

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Temporal variation in the structure and location of dynamic ocean features make them challenging to observe. Beyond requiring sufficient persistence and speed of the sensor platform, effective observation is augmented by autonomous feature detection coupled with adaptive localization of mapping effort. These capabilities enable observations to remain localized within and around features of interest, thereby enhancing spatial and temporal resolution in their depiction. A Wave Glider is a wave‐propelled unmanned surface vehicle with solar energy collection to support payload and a backup propeller in calm sea state. The efficiency of this platform enables persistence (months), and its propulsion system provides speed (~1 m/s). Building on these capabilities, we aim at enabling the Wave Glider to autonomously recognize and track oceanic features that have strong surface expressions, accessible to near‐surface sensors. We present a method of using a Wave Glider to autonomously track an oceanic thermal front known to have biological importance and dynamic evolution. In October 2017, this method was applied to tracking and mapping an upwelling front in Monterey Bay, CA, over 39 hr. The field result demonstrates adaptive localization of mapping based on autonomous feature detection.

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Autonomous tracking of a horizontal boundary

Cited by 4 publications

References 16 publications

Adaptive Sampling of Surface Fronts in the Arctic Using an Autonomous Underwater Vehicle

Adaptive Sampling of Surface Fronts in the Arctic Using an Autonomous Underwater Vehicle

Front delineation and tracking with multiple underwater vehicles

Autonomous tracking of an oceanic thermal front by a Wave Glider

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