Low-Cost Distributed Optical Waveguide Shape Sensor Based on WTDM Applied in Bionics
Bionic robotics, driven by advancements in artificial intelligence, new materials, and manufacturing technologies, is attracting significant attention from research and industry communities seeking breakthroughs. One of the key technologies for achieving a breakthrough in robotics is flexible sensor...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-08-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/23/17/7334 |
| _version_ | 1797581908230537216 |
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| author | Kai Sun Zhenhua Wang Qimeng Liu Hao Chen Weicheng Cui |
| author_facet | Kai Sun Zhenhua Wang Qimeng Liu Hao Chen Weicheng Cui |
| author_sort | Kai Sun |
| collection | DOAJ |
| description | Bionic robotics, driven by advancements in artificial intelligence, new materials, and manufacturing technologies, is attracting significant attention from research and industry communities seeking breakthroughs. One of the key technologies for achieving a breakthrough in robotics is flexible sensors. This paper presents a novel approach based on wavelength and time division multiplexing (WTDM) for distributed optical waveguide shape sensing. Structurally designed optical waveguides based on color filter blocks validate the proposed approach through a cost-effective experimental setup. During data collection, it combines optical waveguide transmission loss and the way of controlling the color and intensity of the light source and detecting color and intensity variations for modeling. An artificial neural network is employed to model and demodulate a data-driven optical waveguide shape sensor. As a result, the correlation coefficient between the predicted and real bending angles reaches 0.9134 within 100 s. To show the parsing performance of the model more intuitively, a confidence accuracy curve is introduced to describe the accuracy of the data-driven model at last. |
| first_indexed | 2024-03-10T23:13:20Z |
| format | Article |
| id | doaj.art-5ece009aa0d445f7b9bc703fffa39e74 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T23:13:20Z |
| publishDate | 2023-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-5ece009aa0d445f7b9bc703fffa39e742023-11-19T08:48:29ZengMDPI AGSensors1424-82202023-08-012317733410.3390/s23177334Low-Cost Distributed Optical Waveguide Shape Sensor Based on WTDM Applied in BionicsKai Sun0Zhenhua Wang1Qimeng Liu2Hao Chen3Weicheng Cui4Zhejiang University-Westlake University Joint Training, Zhejiang University, Hangzhou 310027, ChinaZhejiang University-Westlake University Joint Training, Zhejiang University, Hangzhou 310027, ChinaZhejiang University-Westlake University Joint Training, Zhejiang University, Hangzhou 310027, ChinaZhejiang University-Westlake University Joint Training, Zhejiang University, Hangzhou 310027, ChinaKey Laboratory of Coastal Environment and Resources of Zhejiang Province (KLCER), School of Engineering, Westlake University, Hangzhou 310030, ChinaBionic robotics, driven by advancements in artificial intelligence, new materials, and manufacturing technologies, is attracting significant attention from research and industry communities seeking breakthroughs. One of the key technologies for achieving a breakthrough in robotics is flexible sensors. This paper presents a novel approach based on wavelength and time division multiplexing (WTDM) for distributed optical waveguide shape sensing. Structurally designed optical waveguides based on color filter blocks validate the proposed approach through a cost-effective experimental setup. During data collection, it combines optical waveguide transmission loss and the way of controlling the color and intensity of the light source and detecting color and intensity variations for modeling. An artificial neural network is employed to model and demodulate a data-driven optical waveguide shape sensor. As a result, the correlation coefficient between the predicted and real bending angles reaches 0.9134 within 100 s. To show the parsing performance of the model more intuitively, a confidence accuracy curve is introduced to describe the accuracy of the data-driven model at last.https://www.mdpi.com/1424-8220/23/17/7334optical waveguideWTDMdistributed shape sensorbionics |
| spellingShingle | Kai Sun Zhenhua Wang Qimeng Liu Hao Chen Weicheng Cui Low-Cost Distributed Optical Waveguide Shape Sensor Based on WTDM Applied in Bionics Sensors optical waveguide WTDM distributed shape sensor bionics |
| title | Low-Cost Distributed Optical Waveguide Shape Sensor Based on WTDM Applied in Bionics |
| title_full | Low-Cost Distributed Optical Waveguide Shape Sensor Based on WTDM Applied in Bionics |
| title_fullStr | Low-Cost Distributed Optical Waveguide Shape Sensor Based on WTDM Applied in Bionics |
| title_full_unstemmed | Low-Cost Distributed Optical Waveguide Shape Sensor Based on WTDM Applied in Bionics |
| title_short | Low-Cost Distributed Optical Waveguide Shape Sensor Based on WTDM Applied in Bionics |
| title_sort | low cost distributed optical waveguide shape sensor based on wtdm applied in bionics |
| topic | optical waveguide WTDM distributed shape sensor bionics |
| url | https://www.mdpi.com/1424-8220/23/17/7334 |
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