Acta Photonica Sinica, Volume. 53, Issue 2, 0206006(2024)
Optical Fibre Bragg Based Sliding-tactile Sensing and Classification Training Method for Material Recognition
With the development of smart robots, intelligent tactile sensing is increasingly applied in industrial production, which can greatly improve efficiency and accuracy. Compared with traditional electrical sensors, optical fiber Bragg Grating (FBG) sensors have significant advantages, such as flexibility, electromagnetic immunity, and small size. They also demonstrate high sensitivity and rapid response in perceiving strain and pressure. Current researches on FBG-based tactile sensing mainly focus on strain, temperature, sliding positioning and contact force deduced from the Bragg wavelength shift of FBG. However, there are relatively few researches on combining feature extraction, machine learning, and other cutting-edge technologies to achieve more sophisticated intelligent perception, such as material recognition.In this work, we presented a FBG based sliding-tactile sensing and classification training method for online material recognition by the differential properties of contact surface materials, such as roughness and stick-slip phenomenon. We developed a horizontal two-layer silicone rubber covered FBG sensing unit and its sliding-tactile perception system. When sliding on the certain material, a continuous strain exerts to FBG through the silicone rubber sensing unit and FBG's response changes.To classify efficiently, this paper extracted the mean maximum difference
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Ruizhi PAN, Yan FENG, Hexiang LIU, Haoxiang WANG, Hongpu ZHANG, Yinxiang ZHANG, Hua ZHANG. Optical Fibre Bragg Based Sliding-tactile Sensing and Classification Training Method for Material Recognition[J]. Acta Photonica Sinica, 2024, 53(2): 0206006
Category: Fiber Optics and Optical Communications
Received: Aug. 1, 2023
Accepted: Sep. 14, 2023
Published Online: Mar. 28, 2024
The Author Email: FENG Yan (confirmfyan@163.com)