Multi-vehicle autonomous tracking and filming of white sharks Carcharodon carcharias

Kukulya, Amy; Stokey, R.; Fiester, Carl; Hoyos-Padilla, Mauricio; Skomal, Gregory B.

doi:10.1109/auv.2016.7778707

Cited by 10 publications

(10 citation statements)

References 8 publications

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“…Data collected from previous SharkCam studies showed that great white sharks spend the majority of their time swimming in a straight line at a constant speed (Kukulya et al, , 2016Skomal et al, 2015). Our leatherback turtle study demonstrated that REMUS AUV technology is also capable of making observations of an obligate air breather that dives frequently, is less predictable in its swimming trajectory, and frequently surfaces and hovers.…”

Section: Auv Applicationsmentioning

confidence: 80%

“…To correctly interpret habitat-driven behaviors, we also need to concurrently observe and sample habitat during behavior studies. Autonomous underwater vehicles can efficiently meet all of these objectives, resulting in a more holistic picture of marine animal behavior (Packard et al, 2013;Kukulya et al, 2015Kukulya et al, , 2016Skomal et al, 2015). The pilot study described here demonstrates proof of concept for using an AUV to study leatherback turtle behavior and habitat in a densely populated, high-risk coastal environment, and it can be easily adapted for other species and habitats with similar conservation concerns.…”

Section: Auv Applicationsmentioning

confidence: 96%

“…Recent advancements in autonomous underwater vehicles (AUVs) now make it possible to directly observe subsurface behaviors and concurrently sample the habitats of marine megafauna Kukulya et al, 2016). Here, we describe a "smart" AUV developed to follow and film free-swimming marine animals, and demonstrate the utility of this technology in a pilot study investigating the subsurface behaviors and habitat of leatherback sea turtles (Dermochelys coriacea) in a high-risk coastal environment.…”

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confidence: 99%

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TurtleCam: A “Smart” Autonomous Underwater Vehicle for Investigating Behaviors and Habitats of Sea Turtles

et al. 2018

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Sea turtles inhabiting coastal environments routinely encounter anthropogenic hazards, including fisheries, vessel traffic, pollution, dredging, and drilling. To support mitigation of potential threats, it is important to understand fine-scale sea turtle behaviors in a variety of habitats. Recent advancements in autonomous underwater vehicles (AUVs) now make it possible to directly observe and study the subsurface behaviors and habitats of marine megafauna, including sea turtles. Here, we describe a "smart" AUV capability developed to study free-swimming marine animals, and demonstrate the utility of this technology in a pilot study investigating the behaviors and habitat of leatherback turtles (Dermochelys coriacea). We used a Remote Environmental Monitoring UnitS (REMUS-100) AUV, designated "TurtleCam," that was modified to locate, follow and film tagged turtles for up to 8 h while simultaneously collecting environmental data. The TurtleCam system consists of a 100-m depth rated vehicle outfitted with a circular Ultra-Short BaseLine receiver array for omni-directional tracking of a tagged animal via a custom transponder tag that we attached to the turtle with two suction cups. The AUV collects video with six high-definition cameras (five mounted in the vehicle nose and one mounted aft) and we added a camera to the animal-borne transponder tag to record behavior from the turtle's perspective. Since behavior is likely a response to habitat factors, we collected concurrent in situ oceanographic data (bathymetry, temperature, salinity, chlorophyll-a, turbidity, currents) along the turtle's track. We tested the TurtleCam system during 2016 and 2017 in a densely populated coastal region off Cape Cod, Massachusetts, USA, where foraging leatherbacks overlap with fixed fishing gear and concentrated commercial and recreational vessel traffic. Here we present example data from one leatherback turtle to demonstrate the utility of TurtleCam. The concurrent video, localization, depth and environmental data allowed us to characterize leatherback diving behavior, foraging ecology, and habitat use, and to assess how turtle behavior mediates risk to impacts from anthropogenic activities. Our study demonstrates that an AUV can successfully track and image leatherback turtles feeding in a coastal environment, resulting in novel observations of three-dimensional subsurface behaviors and habitat use, with implications for sea turtle management and conservation.

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Section: Auv Applicationsmentioning

confidence: 80%

Section: Auv Applicationsmentioning

confidence: 96%

mentioning

confidence: 99%

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TurtleCam: A “Smart” Autonomous Underwater Vehicle for Investigating Behaviors and Habitats of Sea Turtles

et al. 2018

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“…Films: Cinema tool, specific cameras used to record marine environments for documental or films [42] At present, AUV-based operations generally require the support of surface vessels and are extremely complex, high-risk, and expensive [9,17]. The use of sophisticated AUV vehicles, which can be either propelled purely via electric thrusters or based on gliding, is also expensive because they need support vessels for their deployment and recovery increasing mission costs [43].…”

Section: Leisurementioning

confidence: 99%

ENDURUNS: An Integrated and Flexible Approach for Seabed Survey Through Autonomous Mobile Vehicles

Marini

Gjeci²,

Govindaraj

et al. 2020

JMSE

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The oceans cover more than two-thirds of the planet, representing the vastest part of natural resources. Nevertheless, only a fraction of the ocean depths has been explored. Within this context, this article presents the H2020 ENDURUNS project that describes a novel scientific and technological approach for prolonged underwater autonomous operations of seabed survey activities, either in the deep ocean or in coastal areas. The proposed approach combines a hybrid Autonomous Underwater Vehicle capable of moving using either thrusters or as a sea glider, combined with an Unmanned Surface Vehicle equipped with satellite communication facilities for interaction with a land station. Both vehicles are equipped with energy packs that combine hydrogen fuel cells and Li-ion batteries to provide extended duration of the survey operations. The Unmanned Surface Vehicle employs photovoltaic panels to increase the autonomy of the vehicle. Since these missions generate a large amount of data, both vehicles are equipped with onboard Central Processing units capable of executing data analysis and compression algorithms for the semantic classification and transmission of the acquired data.

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“…A short baseline system (SBL) hydrophone array in the nose cone determined the bearing and distance toward a large (~ 9 cm × 30 cm) native homing beacon, which was on a suitably large great white shark ( Carcharadon carcharias ) (Packard et al ; Skomal et al ). The AUV continuously turned the vehicle to track the shark, but used the AUV position as proxy for that of the shark (i.e., no tag positions calculated) and required additional assistance from a surface vessel to provide a position (Packard et al ; Kukulya et al ). The AUV would continuously turn toward the shark and occasionally collided with it (Skomal et al ).…”

mentioning

confidence: 99%

Acoustic‐telemetry payload control of an autonomous underwater vehicle for mapping tagged fish

Dodson

Grothues²,

Eiler

et al. 2018

Limnology & Ocean Methods

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Autonomous underwater vehicles (AUVs) have demonstrated superior performance for tracking marine animals tagged with individually coded acoustic transmitters. However, AUVs engaged in mapping the distribution of multiple tagged fish have not previously been able to alter search paths to achieve precise position estimates. This problem is solved by the development of payload control software (Synthetic Aperture Override, SAOVR) that allows the AUV to maneuver with trajectories favorable for solving the tag's location from a synthetic aperture. Upon tag detection during a default mission search path, SAOVR (running on an embedded guest computer) seeks permission to take over navigation from the vehicle's native system after checking constraints of geography, timing, tag identification, signal strength, and current navigation state. Permitted maneuvers are then chosen from a template library and executed before returning the AUV to the point of first deviation for continued searching of other tags. Field evaluation on moored reference tags showed a high level of predictability in the AUV's behavior at SAOVR initiation and through maneuvers. Trials suggest that this logic system is highly beneficial to AUV use for fish telemetry in challenging environments such as narrow, deep fjords, or among reefs. Any mission programmed with the AUV's native software can be run with the SAOVR package to allow scientists to easily implement and manipulate synthetic aperture geometries without altering any of the software. Further modeling can help improve template design specific to expected movements of different fish species and relative to the designation of signal strength-defined execution thresholds.

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Multi-vehicle autonomous tracking and filming of white sharks Carcharodon carcharias

Cited by 10 publications

References 8 publications

TurtleCam: A “Smart” Autonomous Underwater Vehicle for Investigating Behaviors and Habitats of Sea Turtles

TurtleCam: A “Smart” Autonomous Underwater Vehicle for Investigating Behaviors and Habitats of Sea Turtles

ENDURUNS: An Integrated and Flexible Approach for Seabed Survey Through Autonomous Mobile Vehicles

Acoustic‐telemetry payload control of an autonomous underwater vehicle for mapping tagged fish

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