Abstract

Among kinds of flexible tactile sensors, piezoelectric tactile sensor has the advantage of fast response for dynamic force detection. However, it suffers from low sensitivity at high-frequency dynamic stimuli. Here, inspired by finger structure—rigid skeleton embedded in muscle, we report a piezoelectric tactile sensor using a rigid-soft hybrid force-transmission-layer in combination with a soft bottom substrate, which not only greatly enhances the force transmission, but also triggers a significantly magnified effect in d31 working mode of the piezoelectric sensory layer, instead of conventional d33 mode. Experiments show that this sensor exhibits a super-high sensitivity of 346.5 pC N−1 (@ 30 Hz), wide bandwidth of 5–600 Hz and a linear force detection range of 0.009–4.3 N, which is ~17 times the theoretical sensitivity of d33 mode. Furthermore, the sensor is able to detect multiple force directions with high reliability, and shows great potential in robotic dynamic tactile sensing.

Designing efficient tactile sensors under high-frequency dynamic stimuli remains a challenge. Here, the authors demonstrate piezoelectric tactile sensor with sensitivity of 346.5 pCN−1, wide bandwidth of 5–600 Hz and a linear force detection range of 0.009–4.3 N using a rigid-soft hybrid force-transmission-layer in combination with a soft bottom substrate.

Details

Title
Finger-inspired rigid-soft hybrid tactile sensor with superior sensitivity at high frequency
Author
Zhang, Jinhui 1   VIAFID ORCID Logo  ; Yao, Haimin 2   VIAFID ORCID Logo  ; Mo, Jiaying 3 ; Chen, Songyue 1 ; Xie, Yu 1 ; Ma, Shenglin 1 ; Chen, Rui 1 ; Luo, Tao 1   VIAFID ORCID Logo  ; Ling, Weisong 1 ; Qin, Lifeng 1   VIAFID ORCID Logo  ; Wang, Zuankai 4   VIAFID ORCID Logo  ; Zhou, Wei 1   VIAFID ORCID Logo 

 Xiamen University, Department of Mechanical and Electrical Engineering, Xiamen, China (GRID:grid.12955.3a) (ISNI:0000 0001 2264 7233) 
 The Hong Kong Polytechnic University, Hung Hom, Kowloon, Department of Mechanical Engineering, Hong Kong SAR, China (GRID:grid.16890.36) (ISNI:0000 0004 1764 6123) 
 City University of Hong Kong, Department of Mechanical Engineering, Hong Kong, China (GRID:grid.35030.35) (ISNI:0000 0004 1792 6846); Hong Kong Centre for Cerebro-Caradiovasular Health Engineering (COCHE), Hong Kong, China (GRID:grid.35030.35) 
 City University of Hong Kong, Department of Mechanical Engineering, Hong Kong, China (GRID:grid.35030.35) (ISNI:0000 0004 1792 6846) 
Publication year
2022
Publication date
2022
Publisher
Nature Publishing Group
e-ISSN
20411723
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2707731614
Copyright
© The Author(s) 2022. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.