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Optimal Flow Sensing for Schooling Swimmers
Pascal Weber1, Georgios Arampatzis1,2, Guido Novati1
1Computational Science and Engineering Laboratory, ETH Zürich, Clausiusstrasse 33, 8092 Zürich, Switzerland.
Biomimetics (Basel, Switzerland)
|March 19, 2020
Summary
Artificial swimmers can identify leading groups using surface sensors, mimicking fish neuromasts. Optimal sensor placement allows accurate tracking of school
Area of Science:
- Biomimicry in robotics
- Hydrodynamics and fluid dynamics
- Collective animal behavior
Background:
- Fish schooling behavior relies on sensory information beyond vision.
- Pressure and shear sensors are crucial for detecting nearby conspecifics in fluid environments.
- Understanding sensor distribution can inform artificial system design.
Purpose of the Study:
- To determine optimal surface sensor distribution for an artificial swimmer to identify a leading group.
- To investigate the role of pressure and shear sensors in tracking schooling behavior.
- To compare artificial sensor placement with natural fish neuromast distribution.
Main Methods:
- Utilized Bayesian experimental design.
- Performed numerical simulations of two-dimensional Navier-Stokes equations for multiple self-propelled swimmers.
- Analyzed surface pressure and shear stress data on a follower artificial swimmer.
Main Results:
- Identified an optimal sensor distribution for the artificial swimmer.
- Demonstrated that this distribution is qualitatively similar to fish neuromasts.
- Showed accurate identification of the leading group's center of mass and number using surface data alone.
Conclusions:
- Surface-based sensing is sufficient for artificial swimmers to track schools.
- Biomimetic sensor placement can enhance robotic navigation and collective behavior.
- This research provides insights into the principles of hydrodynamic sensing in schooling fish.
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