A Novel Pattern Recognition Method for Self-Powered TENG Sensor Embedded to the Robotic Hand
This paper presents the development and implementation of a human-like robotic hand integrated with advanced triboelectric nanogenerator (TENG) based tactile sensors for shape and material recognition. Meanwhile, traditional piezo sensors’ effectiveness is limited, sensitive to the temper...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10843698/ |
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| author | Azat Balapan Rauan Yeralkhan Alikhan Aryslanov Gulnur Kalimuldina Azamat Yeshmukhametov |
| author_facet | Azat Balapan Rauan Yeralkhan Alikhan Aryslanov Gulnur Kalimuldina Azamat Yeshmukhametov |
| author_sort | Azat Balapan |
| collection | DOAJ |
| description | This paper presents the development and implementation of a human-like robotic hand integrated with advanced triboelectric nanogenerator (TENG) based tactile sensors for shape and material recognition. Meanwhile, traditional piezo sensors’ effectiveness is limited, sensitive to the temperature, and the manufacturing cost is high. TENG sensors offer a self-powered alternative with simplified circuitry, cost-effective fabrication, and enhanced durability. To capitalize on these benefits, we propose a novel machine learning approach that represents time-series data as two-dimensional images processed using a two-dimensional convolutional neural network (2D CNN). This method is compared against the traditional one-dimensional convolutional neural network (1D CNN) method. The research methodology encompasses TENG sensor preparation, noise cancellation, robotic hand design, and control electronics. Experimental results demonstrate that the proposed 2D CNN method significantly improves shape and material recognition accuracy, achieving 98% and 99%, respectively, compared to 94% and 98% with the 1D CNN method. Real-time evaluation further validates the robustness and adaptability of the proposed model in unstructured environments. These findings underscore the potential of integrating TENG sensors with advanced neural network architectures for autonomous dexterous manipulation in various industrial applications, paving the way for future advancements in robotic tactile sensing. |
| format | Article |
| id | doaj-art-7965e83063244445832b3273906adba6 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-7965e83063244445832b3273906adba62025-01-25T00:01:29ZengIEEEIEEE Access2169-35362025-01-0113141011411210.1109/ACCESS.2025.353046510843698A Novel Pattern Recognition Method for Self-Powered TENG Sensor Embedded to the Robotic HandAzat Balapan0Rauan Yeralkhan1Alikhan Aryslanov2Gulnur Kalimuldina3https://orcid.org/0000-0001-9185-3217Azamat Yeshmukhametov4https://orcid.org/0000-0002-6258-8183Department of Robotics Engineering, Nazarbayev University, Astana, KazakhstanDepartment of Mechanical and Aerospace Engineering, Nazarbayev University, Astana, KazakhstanDepartment of Mechanical and Aerospace Engineering, Nazarbayev University, Astana, KazakhstanDepartment of Mechanical and Aerospace Engineering, Nazarbayev University, Astana, KazakhstanDepartment of Robotics Engineering, Nazarbayev University, Astana, KazakhstanThis paper presents the development and implementation of a human-like robotic hand integrated with advanced triboelectric nanogenerator (TENG) based tactile sensors for shape and material recognition. Meanwhile, traditional piezo sensors’ effectiveness is limited, sensitive to the temperature, and the manufacturing cost is high. TENG sensors offer a self-powered alternative with simplified circuitry, cost-effective fabrication, and enhanced durability. To capitalize on these benefits, we propose a novel machine learning approach that represents time-series data as two-dimensional images processed using a two-dimensional convolutional neural network (2D CNN). This method is compared against the traditional one-dimensional convolutional neural network (1D CNN) method. The research methodology encompasses TENG sensor preparation, noise cancellation, robotic hand design, and control electronics. Experimental results demonstrate that the proposed 2D CNN method significantly improves shape and material recognition accuracy, achieving 98% and 99%, respectively, compared to 94% and 98% with the 1D CNN method. Real-time evaluation further validates the robustness and adaptability of the proposed model in unstructured environments. These findings underscore the potential of integrating TENG sensors with advanced neural network architectures for autonomous dexterous manipulation in various industrial applications, paving the way for future advancements in robotic tactile sensing.https://ieeexplore.ieee.org/document/10843698/Dataset collectionmachine learningrobot hand designsignal processingTENG sensor |
| spellingShingle | Azat Balapan Rauan Yeralkhan Alikhan Aryslanov Gulnur Kalimuldina Azamat Yeshmukhametov A Novel Pattern Recognition Method for Self-Powered TENG Sensor Embedded to the Robotic Hand IEEE Access Dataset collection machine learning robot hand design signal processing TENG sensor |
| title | A Novel Pattern Recognition Method for Self-Powered TENG Sensor Embedded to the Robotic Hand |
| title_full | A Novel Pattern Recognition Method for Self-Powered TENG Sensor Embedded to the Robotic Hand |
| title_fullStr | A Novel Pattern Recognition Method for Self-Powered TENG Sensor Embedded to the Robotic Hand |
| title_full_unstemmed | A Novel Pattern Recognition Method for Self-Powered TENG Sensor Embedded to the Robotic Hand |
| title_short | A Novel Pattern Recognition Method for Self-Powered TENG Sensor Embedded to the Robotic Hand |
| title_sort | novel pattern recognition method for self powered teng sensor embedded to the robotic hand |
| topic | Dataset collection machine learning robot hand design signal processing TENG sensor |
| url | https://ieeexplore.ieee.org/document/10843698/ |
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