Tracking the Fine Scale Movements of Fish using Autonomous Maritime Robotics: A Systematic State of the Art Review
Research output: Contribution to journal › Article › peer-review
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In: Ocean Engineering, Vol. 229, 108650, 01.06.2021.
Research output: Contribution to journal › Article › peer-review
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TY - JOUR
T1 - Tracking the Fine Scale Movements of Fish using Autonomous Maritime Robotics: A Systematic State of the Art Review
AU - Nash, John Zachary
AU - Teahan, William
AU - Bond, Jenny
AU - McCarthy, Ian
AU - Pierce, Iestyn
AU - Case, Michael
AU - Mowat, Ryan
PY - 2021/6/1
Y1 - 2021/6/1
N2 - This paper provides a systematic state of the art review on tracking the fine scale movements of fish with the use of autonomous maritime robotics. Knowledge of migration patterns and the localization of specific species of fish at a given time is vital to many aspects of conservation. This paper reviews these technologies and provides insight into what systems are being used and why. The review results show that a larger amount of complex systems that use a deep learning techniques are used over more simplistic approaches to the design. Most results found in the study involve Autonomous Underwater Vehicles, which generally require the most complex array of sensors. The results also provide insight into future research such as methods involving swarm intelligence, which has seen an increase in use in recent years. This synthesis of current and future research will be helpful to research teams working to create an autonomous vehicle with intentions to track, navigate or survey.
AB - This paper provides a systematic state of the art review on tracking the fine scale movements of fish with the use of autonomous maritime robotics. Knowledge of migration patterns and the localization of specific species of fish at a given time is vital to many aspects of conservation. This paper reviews these technologies and provides insight into what systems are being used and why. The review results show that a larger amount of complex systems that use a deep learning techniques are used over more simplistic approaches to the design. Most results found in the study involve Autonomous Underwater Vehicles, which generally require the most complex array of sensors. The results also provide insight into future research such as methods involving swarm intelligence, which has seen an increase in use in recent years. This synthesis of current and future research will be helpful to research teams working to create an autonomous vehicle with intentions to track, navigate or survey.
KW - ASV
KW - AUV
KW - Acoustic telemetry
KW - Fish tracking
KW - Maritime robotics
KW - Underwater robotics
U2 - 10.1016/j.oceaneng.2021.108650
DO - 10.1016/j.oceaneng.2021.108650
M3 - Article
VL - 229
JO - Ocean Engineering
JF - Ocean Engineering
SN - 0029-8018
M1 - 108650
ER -