Collecting, interpreting, and merging fish telemetry data from an AUV: Remote sensing from an already remote platform

Grothues, Thomas M.; Dobarro, Joseph A.; Eiler, John H.

doi:10.1109/auv.2010.5779658

Cited by 17 publications

(22 citation statements)

References 11 publications

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“…Tracking stationary and moving targets with robotic systems is a well-studied field of research [6], [7], [8], [9], [10], [11]. Within the context of using underwater robots to track individuals, both optical based methods [12], [13], [14], and acoustic based methods [15], [16] have been used.…”

Section: Introductionmentioning

confidence: 99%

A multi-AUV state estimator for determining the 3D position of tagged fish

Lin

Kastein

Peterson

et al. 2014

2014 IEEE/RSJ International Conference on Intelligent Robots and Systems

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This paper presents a multi-AUV state-estimator that can determine the 3D position of a tagged fish. In addition to angle measurements, the state-estimator also incorporates distance and depth measurements. These additional sensor measurements allow for greater accuracy in the position estimates. A newly developed motion model that better accounts for multiple hypotheses of the motion of a tagged fish is used to increase the robustness of the state-estimator. A series of multi-AUV shark tracks were conducted at Santa Catalina Island, California over the span of four days to demonstrate the ability of the state-estimator to determine the 3D position of a tagged leopard shark. Additional experiments in which the AUVs tracked a tagged boat of known location were conducted to quantify the performance of the presented state-estimator. Experimental results demonstrate a three-fold decrease in mean state-estimation error compared to previous works.

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Section: Introductionmentioning

confidence: 99%

A multi-AUV state estimator for determining the 3D position of tagged fish

Lin

Kastein

Peterson

et al. 2014

2014 IEEE/RSJ International Conference on Intelligent Robots and Systems

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“…While AUVs often measure environmental conditions that could impact listening conditions [6], moving platforms and dynamic environments create new range of testing challenges. One solution to this challenge is near-real-time triangulation of the acoustic signal using a combination synthetic aperture and known test tag locations [7].…”

Section: Introductionmentioning

confidence: 99%

Factors affecting detection efficiency of mobile telemetry Slocum gliders

et al. 2017

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Background: Acoustic biotelemetry sensors have been fully integrated into a broad range of mobile autonomous platforms; however, estimates of detection efficiency in different environmental conditions are rare. Here, we examined the role of environmental and vehicle factors influencing detection range for two common acoustic receivers, the VEMCO mobile transceiver (VMT) and a VEMCO cabled receiver (VR2c) aboard a Teledyne Slocum glider. We used two gliders, one as a mobile transmitting glider and one as a mobile receiving glider during the fall in the mid-Atlantic coastal region. Results:We found distance between gliders, water depth, and wind speed were the most important factors influencing the detection efficiency of the VMT and the VR2c receivers. Vehicle attitude and orientation had minimal impacts on detection efficiency for both the VMT and VR2c receivers, suggesting that the flight characteristics of the Slocum glider do not inhibit the detection efficiency of these systems. The distance for 20% detection efficiency was approximately 0.4 and 0.6 km for the VMT and VR2c, respectively. The VR2c receivers had significantly lower detection efficiencies than the VMT receiver at distances <0.1 km, but higher detection efficiencies than the VMT at distances >0.1 km.Conclusions: Slocum gliders are effective biotelemetry assets that serve as sentinels along important animal migration corridors. These gliders can help elucidate the relationships between telemetered organisms and in situ habitat. Therefore, estimating the detection ranges of these common telemetry instruments provides an important metric for understanding the spatial scales appropriate for habitat selection inferences.

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“…They can explore the seafloor with no gaps in deep [53][54][55][56] or shallow water [57,58], to inspect submerged structures [59,60] or ship hulls [60,61], take samples in HABs [62], or perform fish monitoring [63] or pest population control [64,65]. AUV mission duration is usually constrained by their endurance.…”

Section: Biosecurity Applications Of Autonomous Surveillancementioning

confidence: 99%

Autonomous surveillance for biosecurity

Jurdak¹,

Elfes²,

Kusý³

et al. 2015

Trends in Biotechnology

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The global movement of people and goods has increased the risk of biosecurity threats and their potential to incur large economic, social, and environmental costs. Conventional manual biosecurity surveillance methods are limited by their scalability in space and time. This article focuses on autonomous surveillance systems, comprising sensor networks, robots, and intelligent algorithms, and their applicability to biosecurity threats. We discuss the spatial and temporal attributes of autonomous surveillance technologies and map them to three broad categories of biosecurity threat: (i) vector-borne diseases; (ii) plant pests; and (iii) aquatic pests. Our discussion reveals a broad range of opportunities to serve biosecurity needs through autonomous surveillance.

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Collecting, interpreting, and merging fish telemetry data from an AUV: Remote sensing from an already remote platform

Cited by 17 publications

References 11 publications

A multi-AUV state estimator for determining the 3D position of tagged fish

A multi-AUV state estimator for determining the 3D position of tagged fish

Factors affecting detection efficiency of mobile telemetry Slocum gliders

Autonomous surveillance for biosecurity

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