Sensor placement optimization in the artificial lateral line using optimal weight analysis combining feature distance and variance evaluation

Dong, Xu; Lv, Zhiyu; Zeng, Haining; Bessaih, Hadjar; Sun, Bing

doi:10.1016/j.isatra.2018.10.039

Cited by 19 publications

(9 citation statements)

References 24 publications

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“…In 2019, Xu et al put forward an optimal weight analysis algorithm combined with feature distance and variance evaluation and 3 indexes to evaluate the performance of the sensor array. They also briefly discussed the optimal number of sensors [49] . This work has provided new ideas for studies in ALL sensors distribution optimization in the future.…”

Section: All Sensors Placement Optimizationmentioning

confidence: 99%

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

2021

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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.

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Section: All Sensors Placement Optimizationmentioning

confidence: 99%

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

2021

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“…The second feature type, i.e. kurtosis and skewness, describes the distribution of data in the time windows, as used before in fluid flow classification [38] and sensor placement optimization [42]. Instead of averaging over the eight sensors, we take the minimal value, maximal value and standard deviation of both as separate features.…”

Section: ) Feature Extractionmentioning

confidence: 99%

Shape Classification Using Hydrodynamic Detection via a Sparse Large-Scale 2D-Sensitive Artificial Lateral Line

et al. 2020

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Artificial lateral lines are fluid flow sensor arrays, bio-inspired by the fish lateral line organ, that measure a local hydrodynamic environment. These arrays are used to detect objects in water, without relying on light, sound, or on an active beacon. This passive sensing method, called hydrodynamic imaging, is complementary to sonar and vision systems and is suitable for collision avoidance and near-field covert sensing. This sensing method has so far been demonstrated on a biological scale from several to tens of centimeters. Here, we present measurements using a large-scale artificial lateral line of 3.5 meters, consisting of eight all-optical 2D-sensitive flow sensors. We measure the fluid flow as produced by the motion of five different objects, towed across a swimming pool. This results in repeatable stimuli, whose measurements demonstrate a complementary aspect of 2D-sensing. These measurements are both used for constructing temporal hydrodynamic signatures, which reflect the object's shape, and for flow-feature based near-field object classification. For the latter, we present a location-invariant feature extraction method which, using an Extreme Learning Machine neural network, results in a classification F1-score up to 98.6% with selected flow features. We find that, compared to the traditional sensing dimension parallel to the sensor array, the novel transverse fluid velocity component bears more information about the object shape. The classification of objects via hydrodynamic imaging thus benefits from 2D-sensing and can be scaled up to a supra biological scale of several meters. INDEX TERMS Hydrodynamic imaging, artificial lateral line, neural network, inverse problem, sensor array, feature selection.

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“…In Siddhartha Verma's work [22], by combining simulations of the Navier-Stokes equations with Bayesian experimental design, they have determined the optimal arrangements of shear stress and pressure gradient sensors. For pressure sensors, in our previous research [23], we proposed an optimal weight analysis algorithm and a comprehensive evaluation system to optimize the sensor placement for the ALLS.…”

Section: Introductionmentioning

confidence: 99%

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

Dong

Zhang

Fan

et al. 2021

Sensors

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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.

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Sensor placement optimization in the artificial lateral line using optimal weight analysis combining feature distance and variance evaluation

Cited by 19 publications

References 24 publications

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

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

Shape Classification Using Hydrodynamic Detection via a Sparse Large-Scale 2D-Sensitive Artificial Lateral Line

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

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