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

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 metho...

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Bibliographic Details
Published in:Applied sciences 2021-08, Vol.11 (16), p.7759
Main Authors: Lin, Xinghua, Qin, Qing, Wang, Xiaoming, Zhang, Junxia
Format: Article
Language:English
Subjects:
Acoustics
Adaptability
Algorithms
Autonomous underwater vehicles
flow sensing
Flow velocity
Lateral line
Machine learning
Mathematical models
Methods
Neural networks
Principal components analysis
Radial basis function
Reynolds number
signal estimation method
Signal processing
underwater robotic
Underwater robots
underwater targets recognition
Volterra series model
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Summary: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.
ISSN:2076-3417
2076-3417
DOI:10.3390/app11167759