Deep learning-assisted object recognition with hybrid triboelectric-capacitive tactile sensor
Abstract Tactile sensors play a critical role in robotic intelligence and human-machine interaction. In this manuscript, we propose a hybrid tactile sensor by integrating a triboelectric sensing unit and a capacitive sensing unit based on porous PDMS. The triboelectric sensing unit is sensitive to t...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Nature Publishing Group
2024-11-01
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| Series: | Microsystems & Nanoengineering |
| Online Access: | https://doi.org/10.1038/s41378-024-00813-2 |
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| _version_ | 1850061804430950400 |
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| author | Yating Xie Hongyu Cheng Chaocheng Yuan Limin Zheng Zhengchun Peng Bo Meng |
| author_facet | Yating Xie Hongyu Cheng Chaocheng Yuan Limin Zheng Zhengchun Peng Bo Meng |
| author_sort | Yating Xie |
| collection | DOAJ |
| description | Abstract Tactile sensors play a critical role in robotic intelligence and human-machine interaction. In this manuscript, we propose a hybrid tactile sensor by integrating a triboelectric sensing unit and a capacitive sensing unit based on porous PDMS. The triboelectric sensing unit is sensitive to the surface material and texture of the grasped objects, while the capacitive sensing unit responds to the object’s hardness. By combining signals from the two sensing units, tactile object recognition can be achieved among not only different objects but also the same object in different states. In addition, both the triboelectric layer and the capacitor dielectric layer were fabricated through the same manufacturing process. Furthermore, deep learning was employed to assist the tactile sensor in accurate object recognition. As a demonstration, the identification of 12 samples was implemented using this hybrid tactile sensor, and an recognition accuracy of 98.46% was achieved. Overall, the proposed hybrid tactile sensor has shown great potential in robotic perception and tactile intelligence. |
| format | Article |
| id | doaj-art-6201ddd1ab6b47748dee2553eac47cac |
| institution | DOAJ |
| issn | 2055-7434 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Nature Publishing Group |
| record_format | Article |
| series | Microsystems & Nanoengineering |
| spelling | doaj-art-6201ddd1ab6b47748dee2553eac47cac2025-08-20T02:50:07ZengNature Publishing GroupMicrosystems & Nanoengineering2055-74342024-11-011011910.1038/s41378-024-00813-2Deep learning-assisted object recognition with hybrid triboelectric-capacitive tactile sensorYating Xie0Hongyu Cheng1Chaocheng Yuan2Limin Zheng3Zhengchun Peng4Bo Meng5Key Laboratory of Optoelectronic Devices and Systems of Ministry of Education and Guangdong Province, College of Physics and Optoelectronic Engineering, Shenzhen UniversityKey Laboratory of Optoelectronic Devices and Systems of Ministry of Education and Guangdong Province, College of Physics and Optoelectronic Engineering, Shenzhen UniversityKey Laboratory of Optoelectronic Devices and Systems of Ministry of Education and Guangdong Province, College of Physics and Optoelectronic Engineering, Shenzhen UniversityKey Laboratory of Optoelectronic Devices and Systems of Ministry of Education and Guangdong Province, College of Physics and Optoelectronic Engineering, Shenzhen UniversityKey Laboratory of Optoelectronic Devices and Systems of Ministry of Education and Guangdong Province, College of Physics and Optoelectronic Engineering, Shenzhen UniversityKey Laboratory of Optoelectronic Devices and Systems of Ministry of Education and Guangdong Province, College of Physics and Optoelectronic Engineering, Shenzhen UniversityAbstract Tactile sensors play a critical role in robotic intelligence and human-machine interaction. In this manuscript, we propose a hybrid tactile sensor by integrating a triboelectric sensing unit and a capacitive sensing unit based on porous PDMS. The triboelectric sensing unit is sensitive to the surface material and texture of the grasped objects, while the capacitive sensing unit responds to the object’s hardness. By combining signals from the two sensing units, tactile object recognition can be achieved among not only different objects but also the same object in different states. In addition, both the triboelectric layer and the capacitor dielectric layer were fabricated through the same manufacturing process. Furthermore, deep learning was employed to assist the tactile sensor in accurate object recognition. As a demonstration, the identification of 12 samples was implemented using this hybrid tactile sensor, and an recognition accuracy of 98.46% was achieved. Overall, the proposed hybrid tactile sensor has shown great potential in robotic perception and tactile intelligence.https://doi.org/10.1038/s41378-024-00813-2 |
| spellingShingle | Yating Xie Hongyu Cheng Chaocheng Yuan Limin Zheng Zhengchun Peng Bo Meng Deep learning-assisted object recognition with hybrid triboelectric-capacitive tactile sensor Microsystems & Nanoengineering |
| title | Deep learning-assisted object recognition with hybrid triboelectric-capacitive tactile sensor |
| title_full | Deep learning-assisted object recognition with hybrid triboelectric-capacitive tactile sensor |
| title_fullStr | Deep learning-assisted object recognition with hybrid triboelectric-capacitive tactile sensor |
| title_full_unstemmed | Deep learning-assisted object recognition with hybrid triboelectric-capacitive tactile sensor |
| title_short | Deep learning-assisted object recognition with hybrid triboelectric-capacitive tactile sensor |
| title_sort | deep learning assisted object recognition with hybrid triboelectric capacitive tactile sensor |
| url | https://doi.org/10.1038/s41378-024-00813-2 |
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