Bionic Artificial Lateral Line Underwater Localization Based on the Neural Network Method

Abstract

The lateral line system is an essential mechanosensory organ for organisms such as fish; it perceives the fluid environment in the near-field through the neuromasts on the lateral line system, supporting behaviors (e.g., obstacle avoidance and predation in fish). Inspired by the near-field perception ability of fish, we propose an artificial lateral line system composed of pressure sensors that respond to a target’s relative position by measuring the pressure change of the target vibration near the lateral line. Based on the shortcomings of the idealized constrained modeling approach, a multilayer perceptron network was built in this paper to process the pressure signal and predict the coordinates on a two-dimensional plane. Previous studies primarily focused on the localization of a single dipole source and rarely considered the localization of multiple vibration sources. In this paper, we explore the localization of numerous dipole sources of the same and different frequency vibrations based on the prediction of the two-dimensional coordinates of double dipoles. The experimental results show that the mutual interference of two vibration sources causes an increase in the localization error. Compared with multiple sources of vibration at the same frequency, the positioning accuracies of various vibration sources at different frequencies are higher. In addition, we explored the effects of the number of sensors on the localization results.

Research on the application of artificial lateral line system for different sensing effects of fish

“…The kinocilium was longer than the stereocilia, which decreased in turn. The thick secretions secreted by supporting cells envelop the tips of the two types of cilia, known as the cupula [19]. When the cupula receives the signal generated by the nerve mound, there is an amplitude of deflecting phenomenon, the signal is transmitted to the two types of cilia, and the cell membrane is stimulated to produce an excited response.…”

A Review of Recent Advancements in Sensors Employed in Unmanned Underwater Vehicles

“…One approach to mimicking marine life is by utilizing neural networks. The neural network modeling method was built to mimic the fish in a complex environment by training over a long period to complete various activities and behaviors, building neural networks to process the received signals [14]. This is a combination approach of using artificial intelligence to collect, store, and recognize patterns created by the data obtained by sensors on this modeled neural network.…”