A robotic sensory system with high spatiotemporal resolution for texture recognition

Bai，Ningning1,2; Xue，Yiheng3; Chen，Shuiqing3; Shi，Lin1; Shi，Junli1; Zhang，Yuan1; Hou，Xingyu1; Cheng，Yu1; Huang，Kaixi1; Wang，Weidong2; Zhang，Jin3; Liu，Yuan4; Guo，Chuan Fei1

2023-12-01

10.1038/s41467-023-42722-4

Nature Communications   影响因子和分区

2041-1723

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.

SCI

英语

NI期刊 ; NI论文

第一 ; 通讯

WOS:001104729800027

2-s2.0-85176396589

Scopus

1.Department of Materials Science and Engineering,Southern University of Science and Technology,Shenzhen,518055,China
2.School of Mechano-Electronic Engineering,Xidian University,Xi’an,710071,China
3.Department of Computer Science and Engineering,Southern University of Science and Technology,Shenzhen,518055,China
4.Department of Physics and TcSUH,University of Houston,Houston,77204,United States

Bai，Ningning,Xue，Yiheng,Chen，Shuiqing,et al. A robotic sensory system with high spatiotemporal resolution for texture recognition[J]. Nature Communications,2023,14(1).

Bai，Ningning.,Xue，Yiheng.,Chen，Shuiqing.,Shi，Lin.,Shi，Junli.,...&Guo，Chuan Fei.(2023).A robotic sensory system with high spatiotemporal resolution for texture recognition.Nature Communications,14(1).

Bai，Ningning,et al."A robotic sensory system with high spatiotemporal resolution for texture recognition".Nature Communications 14.1(2023).