Flow Field Perception of a Moving Carrier Based on an Artificial Lateral Line System

Abstract

At present, autonomous underwater vehicles (AUVs) cannot perceive local environments in complex marine environments, where fish can obtain hydrodynamic information about the surrounding environment through a lateral line. Inspired by this biological function, an artificial lateral line system (ALLS) was built on a moving bionic carrier using the pressure sensor in this paper. When the carrier operated with different speeds in the flow field, the pressure distribution characteristics surrounding the carrier were analyzed by numerical simulation, where the effect of the flow angle between the fluid velocity direction and the carrier navigation direction was considered. The flume experiment was carried out in accordance with the simulation conditions, and the analysis results of the experiment were consistent with those in the simulation. The relationship between pressure and fluid velocity was established by a fitting method. Subsequently, the pressure difference method was investigated to establish a relationship model between the pressure difference on both sides of the carrier and the flow angle. Finally, a back propagation neural network model was used to predict the fluid velocity, flow angle, and carrier speed successfully in the unknown fluid environment. The local fluid environment perception by moving carrier carrying ALLS was studied which may promote the engineering application of the artificial lateral line in the local perception, positioning, and navigation on AUVs.

Hydrodynamical Fingerprint of a Neighbour in a Fish Lateral Line

“…Artificial lateral lines with biomimetic neuromasts have been developed and applied to bioinspired swimming robots (Fan et al, 2002;Yang et al, 2010;Xu and Mohseni, 2017;Liu et al, 2020). Optimal sensor locations for artificial swimmers have been also investigated to improve the efficiency (Verma et al, 2019;Weber et al, 2020), especially when the number of biomimetic neuromasts is limited.…”

Research Development on Fish Swimming

“…Inspired by the nature, different artificial LLS systems that are equipped with a pressure sensor array have been developed to sense the fluid environment (shown in Figure 5). Guijie Liu investigated the effect of speed and flow angle and studied not only near-field detection but also the AUV's pitch motion parameters perception [169,170]. Although the sensing capability has been achieved, the sensitivity and stability of these current systems need to be further improved.…”

“…Guijie Liu used pressure difference matrix to identify the flow field, Tang utilized the free surface wave equation to percept the vibrating sphere and Maertens combined potential flow model with a linear analysis of the boundary layer to improve the existing object identification algorithm [172][173][174]. As for state recognition, a back propagation neural network and convolutional neural network are applied to predict the flow information quickly [169,170], respectively. In addition, Gao held that vorticity control can be conducted precisely based on LLS when fish swim in schools [175].…”

Fish Lateral Line Inspired Flow Sensors and Flow-aided Control: A Review

“…Additionally, Liu et al in 2020, based on a fitting method and a back propagation neural network model, successfully predicted the flow velocity and direction and the moving velocity [57] . The employment of methods of machine learning provided another possibility to sense hydrodynamic information for further study.…”