Performance of neural networks for localizing moving objects with an artificial lateral line

Boulogne, Luuk; Wolf, Ben; Wiering, Marco A.; Netten, Sietse M. van

doi:10.1088/1748-3190/aa7fcb

Cited by 37 publications

(51 citation statements)

References 22 publications

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“…The lowest detectable flow speeds of the artificial neuromasts affect the detection limits for tracking objects using artificial lateral lines (Boulogne et al 2017). We can infer this threshold velocity, at which the expected sensor signal equals noise levels, by taking the ratio of the measured noise in − → δλ to G times the frequency response (table 1).…”

Section: Sensitivity and Dynamic Rangementioning

confidence: 99%

Bio-inspired all-optical artificial neuromast for 2D flow sensing

et al. 2018

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We present the design, fabrication and testing of a novel all-optical 2D flow velocity sensor, inspired by a fish lateral line neuromast. This artificial neuromast consists of optical fibres inscribed with Bragg gratings supporting a fluid force recipient sphere. Its dynamic response is modelled based on the Stokes solution for unsteady flow around a sphere and found to agree with experimental results. Tuneable mechanical resonance is predicted, allowing a deconvolution scheme to accurately retrieve fluid flow speed and direction from sensor readings. The optical artificial neuromast achieves a low frequency threshold flow sensing of 5 mm s −1 and 5 μm s −1 at resonance, with a typical linear dynamic range of 38 dB at 100 Hz sampling. Furthermore, the optical artificial neuromast is shown to determine flow direction within a few degrees.

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Section: Sensitivity and Dynamic Rangementioning

confidence: 99%

Bio-inspired all-optical artificial neuromast for 2D flow sensing

et al. 2018

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“…Other studies have focused on dipole sources in order to develop methods that extract information and optimize the parameters of the sensing devices [49,50]. In a recent study artificial neural networks were employed to classify the environment using flow-only information [51][52][53][54]. In order to find effective sensor positions weight analysis algorithms were employed [55].…”

Section: Introductionmentioning

confidence: 99%

Optimal Flow Sensing for Schooling Swimmers

et al. 2020

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Fish schooling implies an awareness of the swimmers for their companions. In flow mediated environments, in addition to visual cues, pressure and shear sensors on the fish body are critical for providing quantitative information that assists the quantification of proximity to other fish. Here we examine the distribution of sensors on the surface of an artificial swimmer so that it can optimally identify a leading group of swimmers. We employ Bayesian experimental design coupled with numerical simulations of the two-dimensional Navier Stokes equations for multiple self-propelled swimmers. The follower tracks the school using information from its own surface pressure and shear stress. We demonstrate that the optimal sensor distribution of the follower is qualitatively similar to the distribution of neuromasts on fish. Our results show that it is possible to identify accurately the center of mass and the number of the leading swimmers using surface only information.

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“…The positioning of the dipole source was achieved. Boulogne et al [11] used neural networks to identify the location of underwater objects extracted from an ALLS. Ahmad et al [12][13][14] proposed a new ion-exchange polymer metal composite (IPMC)-made sensor distributed in an array on a cylinder to sense underwater dipole sources.…”

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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Performance of neural networks for localizing moving objects with an artificial lateral line

Cited by 37 publications

References 22 publications

Bio-inspired all-optical artificial neuromast for 2D flow sensing

Bio-inspired all-optical artificial neuromast for 2D flow sensing

Optimal Flow Sensing for Schooling Swimmers

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

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