Development of a Flexible Artificial Lateral Line Canal System for Hydrodynamic Pressure Detection

Jiang, Yonggang; Ma, Zhiqiang; Fu, Jianchao; Zhang, Deyuan

doi:10.3390/s17061220

Cited by 42 publications

(41 citation statements)

References 28 publications

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“…Due to the rigid connection between the pillar and the PI film, the bending moment is transferred to the bottom membrane inducing the piezoelectric output. to external flow through a series of pores, assisting fish to identify the pressure distribution in the water [14,15]. Inspired by the LLS of fish, various artificial flow velocity sensors have been developed [16].…”

Section: Sensor Structure and Sensing Principlementioning

confidence: 99%

“…In addition, SNs respond differently to stimulation sources from different directions for the deflection of stereocilia inducing depolarization and hydepolarization of the electrical activity in the hair cell, thus providing spatial flow direction information along the skin surface of fish [12,13]. CNs are located in lateral line canals and connect to external flow through a series of pores, assisting fish to identify the pressure distribution in the water [14,15].…”

Section: Introductionmentioning

confidence: 99%

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Bio-inspired Flexible Lateral Line Sensor Based on P(VDF-TrFE)/BTO Nanofiber Mat for Hydrodynamic Perception

Hu¹,

Jiang²,

Ma³

et al. 2019

Sensors

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Fish and some amphibians can perform a variety of behaviors in confined and harsh environments by employing an extraordinary mechanosensory organ, the lateral line system (LLS). Inspired by the form-function of the LLS, a hydrodynamic artificial velocity sensor (HAVS) was presented in this paper. The sensors featured a polarized poly (vinylidene fluoride-trifluoroethylene) [P(VDF-TrFE)]/barium titanate (BTO) electrospinning nanofiber mat as the sensing layer, a polyimide (PI) film with arrays of circular cavities as the substrate, and a poly(methyl methacrylate) (PMMA) pillar as the cilium. The P(VDF-TrFE)/BTO electrospinning nanofiber mat demonstrated enhanced crystallinity and piezoelectricity compared with the pure P(VDF-TrFE) nanofiber mat. A dipole source was employed to characterize the sensing performance of the fabricated HAVS. The HAVS achieved a velocity detection limit of 0.23 mm/s, superior to the conventional nanofiber mat-based flow sensor. In addition, directivity was feasible for the HAVS, which was in accordance with the simulation results. The proposed bio-inspired flexible lateral line sensor with hydrodynamic perception ability shows promising applications in underwater robotics for real-time flow analysis.

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Section: Sensor Structure and Sensing Principlementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Bio-inspired Flexible Lateral Line Sensor Based on P(VDF-TrFE)/BTO Nanofiber Mat for Hydrodynamic Perception

Hu¹,

Jiang²,

Ma³

et al. 2019

Sensors

Self Cite

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“…Yang et al [7] developed an ALLS using artificial cilia sensors and micro-electromechanical system technology to successfully locate artificial dipole sources. Fu et al [8,9] and Jiang et al [10] laminated polypropylene and polyvinylidene fluoride (PVDF) layers to form cantilever flow sensing elements which were used to develop the ALLSs. The positioning of the dipole source was achieved.…”

Section: Introductionmentioning

confidence: 99%

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

Liu

Hao

Yang

et al. 2020

Sensors

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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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“…The research can be divided into two parts. One is the engineering design of sensors based on setting nature as a model, which is called biomimetics [12][13][14], and the other one is the hydrodynamic mechanism of LLS [15][16][17][18].…”

Section: Introductionmentioning

confidence: 99%

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

Xinghua

Qin

2020

JMSE

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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.

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Development of a Flexible Artificial Lateral Line Canal System for Hydrodynamic Pressure Detection

Cited by 42 publications

References 28 publications

Bio-inspired Flexible Lateral Line Sensor Based on P(VDF-TrFE)/BTO Nanofiber Mat for Hydrodynamic Perception

Bio-inspired Flexible Lateral Line Sensor Based on P(VDF-TrFE)/BTO Nanofiber Mat for Hydrodynamic Perception

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

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

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