Affiliation:
1. Center for Stretchable Electronics and Nano Sensors State Key Laboratory of Radio Frequency Heterogeneous Integration School of Physics and Optoelectronic Engineering Shenzhen University Shenzhen 518060 China
2. School of Science and Engineering The Chinese University of Hong Kong Shenzhen 518172 China
3. Department of Micro-Nano Electronics School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai 200240 China
Abstract
Perceiving surface characteristics through tactile interaction typically requires high‐resolution devices or precise spatial scanning to record and analyze a significant amount of information. However, most available tactile sensors require complicated technological processes, redundant layouts, and data acquisition circuits, which limits their ability to provide a real‐time static perception and feedback for potential applications such as robotic manipulation. Drawing inspiration from the sliding tactile (ST) perception mode of the human fingertip, a robust and flexible ST sensor with a low array density of 2.7 cells cm−2 is reported. This innovative sensor has a soft and cambered configuration that allows it to rapidly and accurately recognize the 3D surface features of objects, including grooves as small as 500 μm. Benefiting from the strong correlation between collected electronic responding and local deformation of sensing cell, the ST sensor can adaptively reconstruct surface patterns with the assistance of deep learning, even on unstructured objects. The pattern recognition system based on the robot is demonstrated by accurately classifying a set of mahjong tiles with nearly 100% accuracy, surpassing human tactile perception capabilities in the same task.
Funder
Basic and Applied Basic Research Foundation of Guangdong Province
National Natural Science Foundation of China
Cited by
3 articles.
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