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CSpace
Highly efficient recognition of similar objects based on ionic robotic tactile sensors
Kong, Yongkang1,2; Cheng, Guanyin1; Zhang, Mengqin1; Zhao, Yongting1; Meng, Wujun1,2; Tian, Xin1; Sun, Bihao1; Yang, Fuping2; Wei, Dapeng1,2
2024-07-15
摘要Tactile sensing provides robots the ability of object recognition, fine operation, natural interaction, etc. However, in the actual scenario, robotic tactile recognition of similar objects still faces difficulties such as low efficiency and accuracy, resulting from a lack of high-performance sensors and intelligent recognition algorithms. In this paper, a flexible sensor combining a pyramidal microstructure with a gradient conformal ionic gel coating was demonstrated, exhibiting excellent signal-to-noise ratio (48 dB), low detection limit (1 Pa), high sensitivity (92.96 kPa(-1)), fast response time (55 ms), and outstanding stability over 15,000 compression-release cycles. Furthermore, a Pressure-Slip Dual-Branch Convolutional Neural Network (PSNet) architecture was proposed to separately extract hardness and texture features and perform feature fusion. In tactile experiments on different kinds of leaves, a recognition rate of 97.16% was achieved, and surpassed that of human hands recognition (72.5%). These researches showed the great potential in a broad application in bionic robots, intelligent prostheses, and precise human-computer interaction. (c) 2024 Science China Press. Published by Elsevier B.V. and Science China Press. All rights are reserved, including those for text and data mining, AI training, and similar technologies.
关键词Tactile recognition Conformal ionic gel coating Flexible sensors Dual-branch convolutional neural network
DOI10.1016/j.scib.2024.04.060
发表期刊SCIENCE BULLETIN
ISSN2095-9273
卷号69期号:13页码:2089-2098
通讯作者Wei, Dapeng(dpwei@cigit.ac.cn)
收录类别SCI
WOS记录号WOS:001270373300001
语种英语