TY - JOUR
T1 - A novel tactile sensing skin for surface perception
AU - Das, Subham
AU - Thiyagarajan, Karthick
AU - Kodagoda, Sarath
AU - Krishnan, Athul
AU - Bhattacharjee, Mitradip
N1 - Publisher Copyright:
© 2001-2012 IEEE.
PY - 2025
Y1 - 2025
N2 - Humans use a fusion of pressure and temperature signals to perceive tactile stimuli. While replicating the complexity of human tactile sensing poses challenges, advancements in artificial tactile sensing skins play a crucial role in the progress of robotics and prosthetics. Here, we present a novel tactile sensing skin designed for the perception of diverse surfaces. It uses a microstructured porous tactile sensor, exhibiting notable sensitivity at 21.5 kPa-1 under low-pressure conditions and 2.5 kPa-1 in high-pressure scenarios. The sensor also boasts an impressively low detection limit of 2.5 Pa, allowing for discernment of subtle pressure variations. Simultaneously, we use a flexible temperature sensor exhibiting a sensitivity of 0.58% °C-1 and swift response attributes to facilitate the detection of radiated heat from diverse surfaces. The incorporation of a multilayer perceptron (MLP) algorithm into the developed glove-like system, equipped with tactile and temperature sensors, achieves 87% accuracy in classifying 11 distinct surfaces.
AB - Humans use a fusion of pressure and temperature signals to perceive tactile stimuli. While replicating the complexity of human tactile sensing poses challenges, advancements in artificial tactile sensing skins play a crucial role in the progress of robotics and prosthetics. Here, we present a novel tactile sensing skin designed for the perception of diverse surfaces. It uses a microstructured porous tactile sensor, exhibiting notable sensitivity at 21.5 kPa-1 under low-pressure conditions and 2.5 kPa-1 in high-pressure scenarios. The sensor also boasts an impressively low detection limit of 2.5 Pa, allowing for discernment of subtle pressure variations. Simultaneously, we use a flexible temperature sensor exhibiting a sensitivity of 0.58% °C-1 and swift response attributes to facilitate the detection of radiated heat from diverse surfaces. The incorporation of a multilayer perceptron (MLP) algorithm into the developed glove-like system, equipped with tactile and temperature sensors, achieves 87% accuracy in classifying 11 distinct surfaces.
UR - https://go.openathens.net/redirector/westernsydney.edu.au?url=https://ieeexplore.ieee.org/document/10990295
UR - http://www.scopus.com/inward/record.url?scp=105004772787&partnerID=8YFLogxK
U2 - 10.1109/JSEN.2025.3565586
DO - 10.1109/JSEN.2025.3565586
M3 - Article
SN - 1530-437X
VL - 25
SP - 23155
EP - 23162
JO - IEEE Sensors Journal
JF - IEEE Sensors Journal
IS - 13
ER -