Abstract

Humans can gently slide a finger on the surface of an object and identify it by capturing both static pressure and high-frequency vibrations. Although modern robots integrated with flexible sensors can precisely detect pressure, shear force, and strain, they still perform insufficiently or require multi-sensors to respond to both static and high-frequency physical stimuli during the interaction. Here, we report a real-time artificial sensory system for high-accuracy texture recognition based on a single iontronic slip-sensor, and propose a criterion—spatiotemporal resolution, to corelate the sensing performance with recognition capability. The sensor can respond to both static and dynamic stimuli (0-400 Hz) with a high spatial resolution of 15 μm in spacing and 6 μm in height, together with a high-frequency resolution of 0.02 Hz at 400 Hz, enabling high-precision discrimination of fine surface features. The sensory system integrated on a prosthetic fingertip can identify 20 different commercial textiles with a 100.0% accuracy at a fixed sliding rate and a 98.9% accuracy at random sliding rates. The sensory system is expected to help achieve subtle tactile sensation for robotics and prosthetics, and further be applied to haptic-based virtual reality and beyond.

Artificial sensory systems are typically limited by their performance and response to static and dynamic stimuli. Here, Bai et al. propose an iontronic slip-sensor, which responds to both static pressure and high-frequency vibrations up to 400 Hz, achieving high spatiotemporal resolution for texture recognition.

Details

Title
A robotic sensory system with high spatiotemporal resolution for texture recognition
Author
Bai, Ningning 1 ; Xue, Yiheng 2   VIAFID ORCID Logo  ; Chen, Shuiqing 2 ; Shi, Lin 3 ; Shi, Junli 3 ; Zhang, Yuan 3 ; Hou, Xingyu 3 ; Cheng, Yu 3 ; Huang, Kaixi 3 ; Wang, Weidong 4   VIAFID ORCID Logo  ; Zhang, Jin 2 ; Liu, Yuan 5 ; Guo, Chuan Fei 3   VIAFID ORCID Logo 

 Southern University of Science and Technology, Department of Materials Science and Engineering, Shenzhen, China (GRID:grid.263817.9) (ISNI:0000 0004 1773 1790); Xidian University, School of Mechano-Electronic Engineering, Xi’an, China (GRID:grid.440736.2) (ISNI:0000 0001 0707 115X) 
 Southern University of Science and Technology, Department of Computer Science and Engineering, Shenzhen, China (GRID:grid.263817.9) (ISNI:0000 0004 1773 1790) 
 Southern University of Science and Technology, Department of Materials Science and Engineering, Shenzhen, China (GRID:grid.263817.9) (ISNI:0000 0004 1773 1790) 
 Xidian University, School of Mechano-Electronic Engineering, Xi’an, China (GRID:grid.440736.2) (ISNI:0000 0001 0707 115X) 
 University of Houston, Department of Physics and TcSUH, Houston, USA (GRID:grid.266436.3) (ISNI:0000 0004 1569 9707) 
Pages
7121
Publication year
2023
Publication date
2023
Publisher
Nature Publishing Group
e-ISSN
20411723
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2889801414
Copyright
© The Author(s) 2023. 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.