Open AccessVirtual Texture Generated Using Elastomeric Conductive Block Copolymer in a Wireless Multimodal Haptic Glove
Author(s) -
Keef Colin V.,
Kayser Laure V.,
Tronboll Stazia,
Carpenter Cody W.,
Root Nicholas B.,
Finn Mickey,
O'Connor Timothy F.,
Abuhamdieh Sami N.,
Davies Daniel M.,
Runser Rory,
Meng Ying Shirley,
Ramachandran Vilayanur S.,
Lipomi Darren J.
Publication year - 2020
Publication title -
advanced intelligent systems
Language(s) - English
Resource type - Journals
ISSN - 2640-4567
DOI - 10.1002/aisy.202000018
Subject(s) - materials science , haptic technology , elastomer , artificial muscle , conductive polymer , surface finish , electrical conductor , computer science , biomedical engineering , actuator , polymer , composite material , simulation , artificial intelligence , medicine
Haptic devices are in general more adept at mimicking the bulk properties of materials than they are at mimicking the surface properties. Herein, a haptic glove is described which is capable of producing sensations reminiscent of three types of near‐surface properties: hardness, temperature, and roughness. To accomplish this mixed mode of stimulation, three types of haptic actuators are combined: vibrotactile motors, thermoelectric devices, and electrotactile electrodes made from a stretchable conductive polymer synthesized in the laboratory. This polymer consists of a stretchable polyanion which serves as a scaffold for the polymerization of poly(3,4‐ethylenedioxythiophene). The scaffold is synthesized using controlled radical polymerization to afford material of low dispersity, relatively high conductivity, and low impedance relative to metals. The glove is equipped with flex sensors to make it possible to control a robotic hand and a hand in virtual reality (VR). In psychophysical experiments, human participants are able to discern combinations of electrotactile, vibrotactile, and thermal stimulation in VR. Participants trained to associate these sensations with roughness, hardness, and temperature have an overall accuracy of 98%, whereas untrained participants have an accuracy of 85%. Sensations can similarly be conveyed using a robotic hand equipped with sensors for pressure and temperature.