Intelligent soft robotic fingers with multi-modality perception ability
Summary: In the context of industry 4.0, automatic sorting is becoming prevalent in production lines. Herein, we developed a bionic sensing system to achieve real-time object recognition. The system consists of 9 single-layer triboelectric nanogenerators (SL-TENGs) as touch sensors and 3 comb-shaped...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-08-01
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| Series: | iScience |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2589004223013263 |
| _version_ | 1797780711487307776 |
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| author | Tongjing Wu Haitao Deng Zhongda Sun Xinran Zhang Chengkuo Lee Xiaosheng Zhang |
| author_facet | Tongjing Wu Haitao Deng Zhongda Sun Xinran Zhang Chengkuo Lee Xiaosheng Zhang |
| author_sort | Tongjing Wu |
| collection | DOAJ |
| description | Summary: In the context of industry 4.0, automatic sorting is becoming prevalent in production lines. Herein, we developed a bionic sensing system to achieve real-time object recognition. The system consists of 9 single-layer triboelectric nanogenerators (SL-TENGs) as touch sensors and 3 comb-shaped TENGs (CS-TENGs) as bending sensors, with a sensitivity of 110 V/kPa and stable output after 20,000 press cycles. These sensors were attached to a manipulator composed of three soft actuators, serving as soft robotic fingers. An enhanced electrical output of these sensors was achieved successfully, demonstrating their feasibility in detecting grasping location, contact pressure, and bending curvature. A one-dimensional convolutional neural network (1D-CNN) with 98.96% accuracy extracted information from the sensors, enabling the manipulator to serve as an intelligent sensing system with multi-modality perception ability. This robotic manipulator successfully integrated TENG-based self-powered sensors, soft actuators, and artificial intelligence, demonstrating the potential for future digital twin applications, particularly in automatic component sorting. |
| first_indexed | 2024-03-12T23:47:51Z |
| format | Article |
| id | doaj.art-dff61b11595a44af9e5b88c9e388df3f |
| institution | Directory Open Access Journal |
| issn | 2589-0042 |
| language | English |
| last_indexed | 2024-03-12T23:47:51Z |
| publishDate | 2023-08-01 |
| publisher | Elsevier |
| record_format | Article |
| series | iScience |
| spelling | doaj.art-dff61b11595a44af9e5b88c9e388df3f2023-07-14T04:28:17ZengElsevieriScience2589-00422023-08-01268107249Intelligent soft robotic fingers with multi-modality perception abilityTongjing Wu0Haitao Deng1Zhongda Sun2Xinran Zhang3Chengkuo Lee4Xiaosheng Zhang5School of Integrated Circuit Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China; Department of Electrical and Computer Engineering, National University of Singapore, 4 Engineering Drive 3, Singapore 117576, SingaporeSchool of Integrated Circuit Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, ChinaDepartment of Electrical and Computer Engineering, National University of Singapore, 4 Engineering Drive 3, Singapore 117576, SingaporeSchool of Integrated Circuit Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, ChinaDepartment of Electrical and Computer Engineering, National University of Singapore, 4 Engineering Drive 3, Singapore 117576, Singapore; Corresponding authorSchool of Integrated Circuit Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China; Corresponding authorSummary: In the context of industry 4.0, automatic sorting is becoming prevalent in production lines. Herein, we developed a bionic sensing system to achieve real-time object recognition. The system consists of 9 single-layer triboelectric nanogenerators (SL-TENGs) as touch sensors and 3 comb-shaped TENGs (CS-TENGs) as bending sensors, with a sensitivity of 110 V/kPa and stable output after 20,000 press cycles. These sensors were attached to a manipulator composed of three soft actuators, serving as soft robotic fingers. An enhanced electrical output of these sensors was achieved successfully, demonstrating their feasibility in detecting grasping location, contact pressure, and bending curvature. A one-dimensional convolutional neural network (1D-CNN) with 98.96% accuracy extracted information from the sensors, enabling the manipulator to serve as an intelligent sensing system with multi-modality perception ability. This robotic manipulator successfully integrated TENG-based self-powered sensors, soft actuators, and artificial intelligence, demonstrating the potential for future digital twin applications, particularly in automatic component sorting.http://www.sciencedirect.com/science/article/pii/S2589004223013263BionicsControl engineeringRobotics |
| spellingShingle | Tongjing Wu Haitao Deng Zhongda Sun Xinran Zhang Chengkuo Lee Xiaosheng Zhang Intelligent soft robotic fingers with multi-modality perception ability iScience Bionics Control engineering Robotics |
| title | Intelligent soft robotic fingers with multi-modality perception ability |
| title_full | Intelligent soft robotic fingers with multi-modality perception ability |
| title_fullStr | Intelligent soft robotic fingers with multi-modality perception ability |
| title_full_unstemmed | Intelligent soft robotic fingers with multi-modality perception ability |
| title_short | Intelligent soft robotic fingers with multi-modality perception ability |
| title_sort | intelligent soft robotic fingers with multi modality perception ability |
| topic | Bionics Control engineering Robotics |
| url | http://www.sciencedirect.com/science/article/pii/S2589004223013263 |
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