Tracking of a tagged leopard shark with an AUV: Sensor calibration and state estimation

Forney, Christina; Manii, Esfandiar; Farris, Michael A.; Moline, Mark A.; Lowe, Christopher G.; Clark, Christopher M.

doi:10.1109/icra.2012.6224991

Cited by 31 publications

(33 citation statements)

References 13 publications

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“…By image identification of bottom fish species and coincidently imaging the seafloor, they were able to assess associations and identify usable habitat. While the example presented here focused on layers and schools of organisms, a similar system could be used to track individual organisms, such as marine mammals and top other predators without the use of tagging as has been demonstrated [21]. More challenging than sampling larger organisms is applying these closed-loop approaches to smaller organisms as these targets are much more difficult to identify in real time.…”

Section: Discussionmentioning

confidence: 99%

Sensor Fusion and Autonomy as a Powerful Combination for Biological Assessment in the Marine Environment

Moline

Benoit‐Bird

2016

Robotics

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Abstract:The ocean environment and the physical and biological processes that govern dynamics are complex. Sampling the ocean to better understand these processes is difficult given the temporal and spatial domains and sampling tools available. Biological systems are especially difficult as organisms possess behavior, operate at horizontal scales smaller than traditional shipboard sampling allows, and are often disturbed by the sampling platforms themselves. Sensors that measure biological processes have also generally not kept pace with the development of physical counterparts as their requirements are as complex as the target organisms. Here, we attempt to address this challenge by advocating the need for sensor-platform combinations to integrate and process data in real-time and develop data products that are useful in increasing sampling efficiencies. Too often, the data of interest is only garnered after post-processing after a sampling effort and the opportunity to use that information to guide sampling is lost. Here we demonstrate a new autonomous platform, where data are collected, analyzed, and data products are output in real-time to inform autonomous decision-making. This integrated capability allows for enhanced and informed sampling towards improving our understanding of the marine environment.

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Section: Discussionmentioning

confidence: 99%

Sensor Fusion and Autonomy as a Powerful Combination for Biological Assessment in the Marine Environment

Moline

Benoit‐Bird

2016

Robotics

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“…This approach would be analogous to someone changing course after hearing a transmitter signal in order to obtain more detailed information, and has numerous applications. Payload control is capable of autonomously following a particular tagged animal in a simple estuarine system (Forney et al, 2012) and further algorithmic developments promise to yield additional improvement (Xydes et al, 2013).…”

Section: Performance Metricsmentioning

confidence: 99%

“…Payload control systems, by which data about ongoing tag detections is used to make navigation decisions, have been developed and tested for proof of concept (Forney et al, 2012), but only for following a single transmitter and never for the purpose of mapping multiple transmitters in a predefi ned search area. To the same end, search path geometries that maximize or fi nd the best compromise between effi ciency and the ability to accurately position a tag from synthetic aperture or sound pressure level methods (Nielsen et al, 2012;Grothues and Davis, 2013) should be modeled and then tested in situ on moving fi sh.…”

Section: Future Research Needsmentioning

confidence: 99%

Comparing Autonomous Underwater Vehicle (AUV) and Vessel-based Tracking Performance for Locating Acoustically Tagged Fish

Eiler¹,

Grothues²,

Dobarro³

et al. 2014

MFR

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“…Since acoustic ranging information is assumed to be available at each of the t steps, the resultant beacon's path has the same length as the survey path. With both the beacon and survey paths, the sum of the positioning errors (12), which is equivalent to the cost, can be calculated. The same simulation is performed using the policies encoded in all the chromosomes in the population, and the resultant fitnesses are ranked in descending order for the selection operation.…”

Section: Fitness Evaluation Through Monte Carlo Simulationmentioning

confidence: 99%

“…In [10] the author suggests some maneuvers for the survey AUV while stating that the beacon vehicle would "most likely sprint and drift off side the survey path to force enough relative motion change to fix vehicle position". More recently, in slightly different application domain, the authors in [12] applied the similar concept in tracking tagged sharks using the AUV. They utilized the particle filter algorithm to track the location of the shark while maneuvering the AUV to other locations where the algorithm will converge.…”

mentioning

confidence: 99%

Direct Policy Search with Variable-Length Genetic Algorithm for Single Beacon Cooperative Path Planning

Teck

2014

Springer Tracts in Advanced Robotics

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This paper focuses on Direct Policy Search (DPS) for cooperative path planning of a single beacon vehicle supporting Autonomous Underwater Vehicles (AUVs) performing surveying missions. Due to lack of availability of GPS signals underwater, the position errors of the AUVs grow with time even though they are equipped with proprioceptive sensors for dead reckoning. One way to minimize this error is to have a moving beacon vehicle with good positioning data transmit its position acoustically from different locations to other AUVs. When the position is received, the AUVs can fuse this data with the range measured from the travel time of acoustic transmission to better estimate their own positions and keep the error bounded. In this work, we address the beacon vehicle's path planning problem which takes into account the position errors being accumulated by the supported survey AUVs. We represent the path planning policy as state-action mapping and employ Variable-Length Genetic Algorithm (VLGA) to automatically discover the number of representative states and their corresponding action mapping. We show the resultant planned paths using the learned policy are able to keep the position errors of the survey AUVs bounded over the mission time.

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Tracking of a tagged leopard shark with an AUV: Sensor calibration and state estimation

Cited by 31 publications

References 13 publications

Sensor Fusion and Autonomy as a Powerful Combination for Biological Assessment in the Marine Environment

Sensor Fusion and Autonomy as a Powerful Combination for Biological Assessment in the Marine Environment

Comparing Autonomous Underwater Vehicle (AUV) and Vessel-based Tracking Performance for Locating Acoustically Tagged Fish

Direct Policy Search with Variable-Length Genetic Algorithm for Single Beacon Cooperative Path Planning

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