A biologically-inspired artificial lateral line

: Observations of collective behaviour in fish lead to the development of a novel design of simple and low-cost artificial lateral line sensor

Abstract

Fish use their lateral line to extract information from the hydrodynamic world around them. The lateral line, comprised of velocity-sensitive superficial neuromasts and acceleration-sensitive canal neuromasts, plays a role in mediating many behaviours, from feeding to shoaling. However, even now, the precise functional mechanisms at work remain unclear. Given the complex, and often cluttered, underwater environment, if we were able to better understand
the mechanisms behind this, and emulate such a sense, it could aid underwater vehicles as they traverse the deep. In this study, 96 cichlid hybrids were bred to generate a spectrum of lateral line characteristics. Cichlids have a relatively recent common ancestor, with species sharing many common traits. The well-developed ’widened’ lateral line of the Aulonocara stuartgranti was chosen as one extreme of this spectrum and the underdeveloped ’narrow’ lateral line of the Otopharynx lithobates as the other; the Aulonocara has been observed hunting benthic prey using these canals in dimly lit conditions, while the Otopharynx is a species that feeds visually in brightly lit environments. Individuals were placed into a shoal of companion fish and shoaling tendencies were observed in both laminar and turbulent flow. Neuromasts were stained in all individuals and their location and number recorded. Using Generalised Linear Mixed Models (GLMMs), we found individuals with greater numbers of head canal neuromasts or with larger head canal pores tended to swim closer to neighbours while those with greater numbers of head superficial neuromasts swam in looser shoals. A novel design of bio-inspired sensor that is able to detect a fish-like vortex wake was then created and optimised, both in simulation and experiment. This minimalist sensor is low-cost, low-power, and easy to manufacture, with a number of potential applications including flow monitoring in pipes, supplementary navigation systems in Autonomous Underwater Vehicles (AUVs) or simple flow sensing robots.

Date of Award	22 Mar 2022
Original language	English
Awarding Institution	University of Bristol
Supervisor	Sabine Hauert (Supervisor), Christos C Ioannou (Supervisor) & Martin J Genner (Supervisor)