TY - GEN
T1 - Texture analysis using a piezoelectric actuator-sensor pair
AU - Chung, Jaehoon
AU - Lim, Myotaeg
AU - Cha, Youngsu
N1 - Funding Information:
This work was supported by the Technology Innovation Program for Development of robotic work control technology capable of grasping and manipulating various objects in everyday life environment based on multimodal recognition and using tools funded by the Ministry of Trade, Industry, and Energy under grant number 20001856. The authors would like to thanks Chaewon Oh for helps with drawing schematics.
Publisher Copyright:
© 2020 SPIE.
PY - 2020
Y1 - 2020
N2 - In this paper, we propose a piezoelectric actuator-sensor pair that can classify several objects. It consists of two polyvinylidene-fluoride films above a polyethylene-terephthalate substrate. Herein, the actuator is connected to an voltage supplier, and the sensor output signal is acquired through a measuring equipment. Specifically, this pair is installed on a robot hand. When the objects are grasped by the robot hand in static state, the actuator oscillates as sinusoidal input voltages with frequency sweep are applied for a few seconds. At the same time, the sensor data is obtained and undergoes preprocessing procedure for learning process. The neural network classifier model is trained by learning process. After conducting the learning process, we test the feasibility of the actuator-sensor pair by demonstrating the real-time recognition system.
AB - In this paper, we propose a piezoelectric actuator-sensor pair that can classify several objects. It consists of two polyvinylidene-fluoride films above a polyethylene-terephthalate substrate. Herein, the actuator is connected to an voltage supplier, and the sensor output signal is acquired through a measuring equipment. Specifically, this pair is installed on a robot hand. When the objects are grasped by the robot hand in static state, the actuator oscillates as sinusoidal input voltages with frequency sweep are applied for a few seconds. At the same time, the sensor data is obtained and undergoes preprocessing procedure for learning process. The neural network classifier model is trained by learning process. After conducting the learning process, we test the feasibility of the actuator-sensor pair by demonstrating the real-time recognition system.
KW - Actuator-sensor pair
KW - Object classification
KW - Piezoelectric material
UR - http://www.scopus.com/inward/record.url?scp=85085729602&partnerID=8YFLogxK
U2 - 10.1117/12.2557976
DO - 10.1117/12.2557976
M3 - Conference contribution
AN - SCOPUS:85085729602
T3 - Proceedings of SPIE - The International Society for Optical Engineering
BT - Active and Passive Smart Structures and Integrated Systems IX
A2 - Han, Jae-Hung
A2 - Wang, Gang
A2 - Shahab, Shima
PB - SPIE
T2 - Active and Passive Smart Structures and Integrated Systems IX 2020
Y2 - 27 April 2020 through 8 May 2020
ER -