Robust Flow Field Signal Estimation Method for Flow Sensing by Underwater Robotics

Xinghua Lin,Xiaoming Wang,Junxia Zhang,Qing Qin

doi:10.3390/app11167759

Abstract

The flow field is difficult to evaluate, and underwater robotics can only partly adapt to the submarine environment. However, fish can sense the complex underwater environment by their lateral line system. In order to reveal the fish flow sensing mechanism, a robust nonlinear signal estimation method based on the Volterra series model with the Kautz kernel function is provided, which is named KKF-VSM. The flow field signal around a square target is used as the original signal. The sinusoidal noise and the signal around a triangular obstacle are considered undesired signals, and the predicting performance of KKF-VSM is analyzed after introducing them locally in the original signals. Compared to the radial basis function neural network model (RBF-NNM), the advantages of KKF-VSM are not only its robustness but also its higher sensitivity to weak signals and its predicting accuracy. It is confirmed that even for strong nonlinear signals, such as pressure responses in the flow field, KKF-VSM is more efficient than the commonly used RBF-NNM. It can provide a reference for the application of the artificial lateral line system on underwater robotics, improving its adaptability in complex environments based on flow field information.

Highlights

It is confirmed that even for strong nonlinear signals, such as pressure responses in the flow field, KKF-VSM is more efficient than the commonly used radial basis function neural network model (RBF-NNM). It can provide a reference for the application of the artificial lateral line system on underwater robotics, improving its adaptability in complex environments based on flow field information
The results showed that the RBF-NNM has higher predicting efficiency than the BP network model [42]
In order to estimate the flow field signal change based on the flow sensing mechanism

Summary

Introduction

Since the underwater environment is complex, the adaptability of AUV needs to be improved [1,2]. It has been found that the poor environmental adaptability of AUV is due to its inability to sense the surrounding environment [8,9,10]. AUVs sense the surrounding environment on the basis of a visual and an acoustic system. In the context of underwater target detection, the acoustic signal has many issues, such as high cost and high-power consumption. The visual and acoustic signals detect targets through reflection and are, indirect signals. They are unable to capture information regarding the flow field, such as the eddy and ocean currents

Methods

Results

Conclusion