Design and fabrication of artificial lateral line flow sensors

Fan, Zhifang; Chen, Jack; Zou, Jun; Bullen, David; Liu, Chang; Delcomyn, Fred

doi:10.1088/0960-1317/12/5/322

Cited by 245 publications

(157 citation statements)

References 26 publications

Supporting

Mentioning

153

Contrasting

Unclassified

Order By: Relevance

“…Modelling the pressure sensing after natural or artificial devices (Fan et al 2002;Coombs & Braun 2003;Yang et al 2006), we assume a finite noise-tosignal ratio g in that measurement. This noise level primarily accounts for two sources of ubiquitous noise: the noise induced by the limited capabilities of the mechanotransducers forming the sensing device as well as the signal postprocessing; and the background noise of the fluid flow in which the sensor is placed.…”

Section: Problem Definitionmentioning

confidence: 99%

“…Manoeuvrability requires acute sensing capabilities which can more easily be obtained through multi-modal sensory systems and devices. From the robotic standpoint, Fan et al (2002) reported the first design and fabrication of an artificial lateral line flow sensor. Later, Yang et al (2006) reported the first experimentations where such an artificial LLS is used to detect both a dipole and a wake, hence allowing it to mimic the pressure-sensing capabilities of those encountered in nature.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Hydrodynamic object recognition using pressure sensing

2010

View full text Add to dashboard Cite

Hydrodynamic sensing is instrumental to fish and some amphibians. It also represents, for underwater vehicles, an alternative way of sensing the fluid environment when visual and acoustic sensing are limited. To assess the effectiveness of hydrodynamic sensing and gain insight into its capabilities and limitations, we investigated the forward and inverse problem of detection and identification, using the hydrodynamic pressure in the neighbourhood, of a stationary obstacle described using a general shape representation. Based on conformal mapping and a general normalization procedure, our obstacle representation accounts for all specific features of progressive perceptual hydrodynamic imaging reported experimentally. Size, location and shape are encoded separately. The shape representation rests upon an asymptotic series which embodies the progressive character of hydrodynamic imaging through pressure sensing. A dynamic filtering method is used to invert noisy nonlinear pressure signals for the shape parameters. The results highlight the dependence of the sensitivity of hydrodynamic sensing not only on the relative distance to the disturbance but also its bearing.

show abstract

Section: Problem Definitionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Hydrodynamic object recognition using pressure sensing

2010

View full text Add to dashboard Cite

show abstract

“…Fan et al [19] have made an ALLS by fabricating hair flow sensors, using microelectromechanical systems (MEMS) technology. Franosch et al [30] have developed an ALLS prototype with a single hot-thermistor anemometric sensor positioned onto an underwater robotic system.…”

Section: Lateral-line Systemmentioning

confidence: 99%

Imaging dipole flow sources using an artificial lateral-line system made of biomimetic hair flow sensors

Dagamseh

Wiegerink

Lammerink

et al. 2013

J. R. Soc. Interface.

View full text Add to dashboard Cite

In Nature, fish have the ability to localize prey, school, navigate, etc., using the lateral-line organ. Artificial hair flow sensors arranged in a linear array shape (inspired by the lateral-line system (LSS) in fish) have been applied to measure airflow patterns at the sensor positions. Here, we take advantage of both biomimetic artificial hair-based flow sensors arranged as LSS and beamforming techniques to demonstrate dipole-source localization in air. Modelling and measurement results show the artificial lateral-line ability to image the position of dipole sources accurately with estimation error of less than 0.14 times the array length. This opens up possibilities for flowbased, near-field environment mapping that can be beneficial to, for example, biologists and robot guidance applications.

show abstract

“…This design is favoured since the protruding structures escape unwanted boundary layer effects. Several techniques used for strain sensing include lamella mechanical micro-sensors (MEMs) (Fan et al 2002, McConney et al 2009, Yang et al 2007, 2010, ionic polymer-metal composites (IPMC) (Abdulsadda and Tan 2012, Chen et al 2013 and soft polymer membranes without (Asadnia et al 2013) and with cantilever structures (Kottapalli et al 2014. A technical review of recent contributions to the field of artificial neuromast and artificial lateral lines (ALL) shows an increasing interest in this field (Liu et al 2016).…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2018

View full text Add to dashboard Cite

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.

show abstract

Design and fabrication of artificial lateral line flow sensors

Cited by 245 publications

References 26 publications

Hydrodynamic object recognition using pressure sensing

Hydrodynamic object recognition using pressure sensing

Imaging dipole flow sources using an artificial lateral-line system made of biomimetic hair flow sensors

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

Contact Info

Product

Resources

About