Optimal sensing for fish school identification

TL;DR

This study uses Bayesian experimental design and fluid dynamics simulations to optimize sensor placement on artificial fish for accurate school detection, mimicking biological neuromast distribution.

Contribution

It introduces an optimal sensor distribution method for artificial swimmers to identify fish schools using surface pressure and shear sensors, inspired by biological neuromasts.

Findings

Sensor distribution resembles biological neuromasts.

Accurate identification of school center of mass.

Potential to determine number of swimmers from surface data.

Abstract

Fish schooling implies an awareness of the swimmers for their companions. In flow mediated environments, in addition to visual cues, pressure and shear sensors on the fish body are critical for providing quantitative information that assists the quantification of proximity to other swimmers. Here we examine the distribution of sensors on the surface of an artificial swimmer so that it can optimally identify a leading group of swimmers. We employ Bayesian experimental design coupled with two-dimensional Navier Stokes equations for multiple self-propelled swimmers. The follower tracks the school using information from its own surface pressure and shear stress. We demonstrate that the optimal sensor distribution of the follower is qualitatively similar to the distribution of neuromasts on fish. Our results show that it is possible to identify accurately the center of mass and even the number of the leading swimmers using surface only information.