A Stiffness Controllable Multimodal Whisker Sensor Follicle for Texture Comparison

Mammals like rats, who live in dark burrows, heavily depend on tactile perception obtained through the vibrissal system to move through gaps and to discriminate textures. The organization of a mammalian whisker follicle contains multiple sensory receptors and glands strategically organized to captur...

Full description

Saved in:
Bibliographic Details
Published in:IEEE sensors journal 2020-03, Vol.20 (5), p.2320-2328
Main Authors: Wegiriya, Hasitha, Herzig, Nicolas, Abad, Sara-Adela, Hadi Sadati, S.M., Nanayakkara, Thrishantha
Format: Article
Language:English
Subjects:
Actuators
bio-inspired sensors
Carbon
Magnetic sensors
Magnetomechanical effects
multimodal sensors
Receptors
Robot sensing systems
Shafts
soft robotic sensor
Springs
Stiffness
stiffness controllable sensors
Tactile discrimination
tactile sensors
Texture
Whisker sensors
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Mammals like rats, who live in dark burrows, heavily depend on tactile perception obtained through the vibrissal system to move through gaps and to discriminate textures. The organization of a mammalian whisker follicle contains multiple sensory receptors and glands strategically organized to capture tactile sensory stimuli of different frequencies. In this paper, we used a controllable stiffness soft robotic follicle to test the hypothesis that the multimodal sensory receptors together with the controllable stiffness tissues in the whisker follicle form a physical structure to maximize tactile information. In our design, the ring sinus and ringwulst of a biological follicle are represented by a linear actuator connected to a stiffness controllable mechanism in-between two different frequency-dependent data capturing modules. In this paper, we show for the first time the effect of the interplay between the stiffness and the speed of whisking on maximizing a difference metric for texture classification.
ISSN:1530-437X
1558-1748
DOI:10.1109/JSEN.2019.2951755