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

Lin, Yu-Kun; Kastein, Hannah; Peterson, Taylor; White, Connor F.; Lowe, Christopher G.; Clark, Christopher M.

doi:10.1109/iros.2014.6943046

Cited by 11 publications

(13 citation statements)

References 22 publications

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“…Optical-based method's accuracy is dependent on visibility and variation in illumination of particle-filled underwater scenes [11]- [13]. Acoustic-based method's accuracy is affected by deficiencies in AUV's path-planning and motion algorithms [14], [15].…”

Section: Methodsmentioning

confidence: 99%

Multiple signal classification (MUSIC) based underwater acoustic localization module (UALM) for AUV

Vivek

Vadakkepat

2015

2015 IEEE Underwater Technology (UT)

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Underwater acoustic source localization is important during disaster-rescue missions ranging from rescuing survivors to recovering acoustically-tagged artifacts such as black box. Designing acoustic localization capabilities reliably and efficiently is challenging. Underwater acoustic localization module (UALM) utilizing multiple signal classification (MUSIC) is implemented on an autonomous underwater vehicle (AUV). A state-machine based application program interface (API) is utilized on the AUV operating on robot operating system (ROS). An array of four pre-calibrated hydrophones is utilized for geometry-dependent MUSIC computation. A modular localization approach improved UALM's responsiveness.

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Section: Methodsmentioning

confidence: 99%

Multiple signal classification (MUSIC) based underwater acoustic localization module (UALM) for AUV

Vivek

Vadakkepat

2015

2015 IEEE Underwater Technology (UT)

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“…Key components of the system include a circle tracking controller and state estimator presented in Tang, Shinzaki, Lowe, & Clark () and Lin et al. ().…”

Section: Introductionmentioning

confidence: 99%

A Multi‐Autonomous Underwater Vehicle System for Autonomous Tracking of Marine Life

et al. 2016

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This paper presents a multi‐autonomous underwater vehicle system capable of cooperatively and autonomously tracking and following marine targets (i.e., fish) tagged with an acoustic transmitter. The AUVs have been equipped with stereo‐hydrophones that receive signals broadcasted by the acoustic transmitter tags to enable real‐time calculation of bearing‐to‐tag and distance‐to‐tag measurements. These measurements are shared between AUVs via acoustic modem and fused within each AUV's particle filter for estimating the target's position. The AUVs use a leader/follower multi‐AUV control system to enable the AUVs to drive toward the estimated target state by following collision‐free paths. Once within the local area of the target, the AUVs circumnavigate the target state until it moves to another area. The system builds on previous work by incorporating a new SmartTag package that can be attached to an individual's dorsal fin. The SmartTag houses a full inertial measurement unit (INU), video logger, acoustic transmitter, and timed release mechanism. After real‐time AUV tracking experiments, the SmartTag is recovered. Logged IMU data are fused with logged AUV‐obtained acoustic tag measurements within a particle filter to improve state estimation accuracy. This improvement is validated through a series of multi‐AUV shark and boat tracking experiments conducted at Santa Catalina Island, California. When compared with previous work that did not use the SmartTag package, results demonstrated a decrease in mean position estimation error of 25–75%, tag orientation estimation errors dropped from 80° to 30° , the sensitivity of mean position error with respect to distance to the tag was less by a factor of 50, and the sensitivity of mean position error with respect to acoustic signal reception frequency to the tag was 25 times less. These statistics demonstrate a large improvement in the system's robustness when the SmartTag package is used.

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“…Although the focus was on a single individual, this advancement allowed the use of small, individually coded and commercially available acoustic tags appropriate to use on many species. This work was furthered to include two stereo vehicles, which expands the geometry of the locations over which detections are simultaneously made for determining position from signal time‐of‐flight differences (Lin et al , ; Shinzaki et al ). The AUVs need to be in contact with each other to coordinate movement, avoid collision, and combine the data that they collect independently so that the fused data can be used in a particle filter model to estimate position.…”

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

Cited by 11 publications

References 22 publications

Multiple signal classification (MUSIC) based underwater acoustic localization module (UALM) for AUV

Multiple signal classification (MUSIC) based underwater acoustic localization module (UALM) for AUV

A Multi‐Autonomous Underwater Vehicle System for Autonomous Tracking of Marine Life

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

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