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Fish and Robots Swimming Together in a Water Tunnel: Robot Color and Tail-Beat Frequency Influence Fish Behavior

Overview of attention for article published in PLOS ONE, October 2013
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Title
Fish and Robots Swimming Together in a Water Tunnel: Robot Color and Tail-Beat Frequency Influence Fish Behavior
Published in
PLOS ONE, October 2013
DOI 10.1371/journal.pone.0077589
Pubmed ID
Authors

Giovanni Polverino, Paul Phamduy, Maurizio Porfiri

Abstract

The possibility of integrating bioinspired robots in groups of live social animals may constitute a valuable tool to study the basis of social behavior and uncover the fundamental determinants of animal functions and dysfunctions. In this study, we investigate the interactions between individual golden shiners (Notemigonus crysoleucas) and robotic fish swimming together in a water tunnel at constant flow velocity. The robotic fish is designed to mimic its live counterpart in the aspect ratio, body shape, dimension, and locomotory pattern. Fish positional preference with respect to the robot is experimentally analyzed as the robot's color pattern and tail-beat frequency are varied. Behavioral observations are corroborated by particle image velocimetry studies aimed at investigating the flow structure behind the robotic fish. Experimental results show that the time spent by golden shiners in the vicinity of the bioinspired robotic fish is the highest when the robot mimics their natural color pattern and beats its tail at the same frequency. In these conditions, fish tend to swim at the same depth of the robotic fish, where the wake from the robotic fish is stronger and hydrodynamic return is most likely to be effective.

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Geographical breakdown

Country Count As %
United States 2 3%
Germany 1 1%
Unknown 74 96%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 26 34%
Student > Bachelor 12 16%
Researcher 7 9%
Student > Master 6 8%
Professor 5 6%
Other 9 12%
Unknown 12 16%
Readers by discipline Count As %
Engineering 23 30%
Agricultural and Biological Sciences 19 25%
Computer Science 6 8%
Neuroscience 3 4%
Biochemistry, Genetics and Molecular Biology 3 4%
Other 9 12%
Unknown 14 18%