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

2021 ◽  
Vol 11 (16) ◽  
pp. 7759
Author(s):  
Xinghua Lin ◽  
Qing Qin ◽  
Xiaoming Wang ◽  
Junxia Zhang
Keyword(s):  
Flow Field ◽  
Lateral Line ◽  
Volterra Series ◽  
Estimation Method ◽  
Lateral Line System ◽  
Signal Estimation ◽  
Underwater Robotics ◽  
Line System ◽  
Flow Sensing ◽  
Predicting Performance

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.

scholarly journals Robust Classification Method for Underwater Targets Using the Chaotic Features of the Flow Field

2020 ◽  
Vol 8 (2) ◽  
pp. 111
Author(s):  
Xinghua Lin ◽  
Jianguo Wu ◽  
Qing Qin
Keyword(s):  
Flow Field ◽  
Lateral Line ◽  
Autonomous Underwater Vehicles ◽  
Flow Fields ◽  
Support Vector ◽  
Largest Lyapunov Exponent ◽  
Lateral Line System ◽  
Line System ◽  
Feature Extraction Method ◽  
New Findings

Fish can sense their surrounding environment by their lateral line system (LLS). In order to understand the extent to which information can be derived via LLS and to improve the adaptive ability of autonomous underwater vehicles (AUVs), a novel strategy is presented, which directly uses the information of the flow field to distinguish the object obstacle. The flow fields around different targets are obtained by the numerical method, and the pressure signal on the virtual lateral line is studied based on the chaos theory and fast Fourier transform (FFT). The compounded parametric features, including the chaotic features (CF) and the power spectrum density (PSD), which is named CF-PSD, are used to recognize the kinds of obstacles. During the research of CF, the largest Lyapunov exponent (LLE), saturated correlation dimension (SCD), and Kolmogorov entropy (KE) are taken into account, and PSD features include the number, amplitude, and position of wave crests. A two-step support vector machine (SVM) is built and used to classify the shapes and incidence angles based on the CF-PSD. It is demonstrated that the flow fields around triangular and square targets are chaotic systems, and the new findings indicate that the object obstacle can be recognized directly based on the information of the flow field, and the consideration of a parametric feature extraction method (CF-PSD) results in considerably higher classification success.

Flow field perception based on the fish lateral line system

2019 ◽  
Vol 14 (4) ◽  
pp. 041001 ◽  
Author(s):  
Yonggang Jiang ◽  
Zhiqiang Ma ◽  
Deyuan Zhang
Keyword(s):  
Flow Field ◽  
Lateral Line ◽  
Lateral Line System ◽  
Line System

scholarly journals Optimal Sensor Placement of the Artificial Lateral Line for Flow Parametric Identification

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 3980
Author(s):  
Dong Xu ◽  
Yuanlin Zhang ◽  
Jian Tian ◽  
Hongjie Fan ◽  
Yifan Xie ◽  
...  
Keyword(s):  
Flow Field ◽  
Lateral Line ◽  
Control Function ◽  
Sensor Placement ◽  
Parametric Identification ◽  
Lateral Line System ◽  
Optimal Sensor Placement ◽  
Line System ◽  
Loop Control ◽  
Feature Importance

The multi-sensor artificial lateral line system (ALLS) can identify the flow-field’s parameters to realize the closed-loop control of the underwater robotic fish. An inappropriate sensor placement of ALLS may result in inaccurate flow-field parametric identification. Therefore, this paper proposes a method to optimize the sensor placement configuration of the ALLS, which mainly included three algorithms, the feature importance algorithm based on mean and variance (MVF), the feature importance algorithm based on distance evaluation (DF), and the information redundancy (IR) algorithm. The optimal sensor placement performance selected by this method is verified by simulation. In addition, further experimental verification was conducted using the ALLS. Compared with the uniform sensor placement configuration mentioned in recent studies, the experimental results suggest that the optimal sensor placement method can achieve a more effective prediction of the flow-field parameters, therefore strengthening the underwater robotic fish’s perception and control function.

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

Sensors ◽  
2020 ◽  
Vol 20 (5) ◽  
pp. 1512 ◽  
Author(s):  
Guijie Liu ◽  
Huanhuan Hao ◽  
Tingting Yang ◽  
Shuikuan Liu ◽  
Mengmeng Wang ◽  
...  
Keyword(s):  
Flow Field ◽  
Lateral Line ◽  
Pressure Difference ◽  
Autonomous Underwater Vehicles ◽  
Fluid Velocity ◽  
Engineering Application ◽  
Lateral Line System ◽  
Flow Angle ◽  
Line System ◽  
Fluid Environment

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.

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