Research on Artificial Lateral Line Perception of Flow Field based on Pressure Difference Matrix

In nature,with the help of lateral lines,fish is capable of sensing the state of the flow field and recognizing the surrounding near-fleld hydrodynamic environment in the condition of weak light or even complete darkness.In order to study the application of lateral lines,an improved pressure distribution model was proposed in this paper,and the pressure distributions of the lateral line carrier under different working conditions were obtained using hydrodynamic simulations.Subsequently,a visualized pressure difference matrix was constructed to identify the flow fields under different working conditions.The role of the lateral lines was investigated from a visual image perspective.Instinct features of different flow velocities,flow angles and obstacle offset distances were mapped into the pressure difference matrix.Lastly,a four-layer Convolutional Neural Network(CNN)model was built as a recognition tool to evaluate the effectiveness of the pressure difference matrix method.The recognition results demonstrate that the CNN can identify the flow field state with 2 s earlier than the current time.Hence,the proposed method provides a new way to identify flow field information in engineering applications.

Data Processing Methods of Flow Field Based on Artificial Lateral Line Pressure Sensors

The estimation of the type and parameter of flow field is important for robotic fish.Recent estimation methods cannot meet the requirements of the robotic fish due to the lack of prior knowledge or the under-fitting of the model.A processing method including data preprocessing,feature extraction,feature selection,flow type classification and flow field parameters estimation,is proposed based on the data of the pressure sensors in an artificial lateral line.Probabilistic Neural Network(PNN)is used to classify the flow field type and the Generalized Regressive Neural Network(GRNN)is the best choice for estimating the flow field parameters.Also,a few filtering methods for data preprocessing,three methods for feature selection and nine parameters estimation methods are analysis for choosing better method.The proposed method is verified by the experiments with both simulation and real data.

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

Any phenomenon in nature is potential to be an inspiration for us to propose new ideas.Lateral line is a typical example which has attracted more interest in recent years.With the aid of lateral line,fish is capable of acquiring fluid information around,which is of great significance for them to survive,communicate and hunt underwater.In this paper,we briefly introduce the morphology and mechanism of the lateral line first.Then we focus on the development of artificial lateral line which typically consists of an array of sensors and can be installed on underwater robots.A series of sensors inspired by the lateral line with different sensing principles have been summarized.And then the applications of artificial lateral line systems in hydrodynamic environment sensing and vortices detection,dipole oscillation source detection,and autonomous control of underwater robots have been reviewed.In addition,the existing problems and future foci in this field have been further discussed in detail.The current works and future foci have demonstrated that artificial lateral line has great potentials of applications and contributes to the development of underwater robots.