Fast and accurate object recognition based on tactile data by using Dendrite Net
Tactile perception plays an important role in a variety of applications such as object detection and robotic grasping of objects. However, there is a lack of an intelligent object recognition algorithm based on tactile data with small storage space, fast computing speed, and high accuracy. Inspired...
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| Vydáno v: | IEEE International Instrumentation and Measurement Technology Conference (Online) s. 01 - 06 |
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| Hlavní autoři: | , , , , |
| Médium: | Konferenční příspěvek |
| Jazyk: | angličtina |
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IEEE
22.05.2023
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| ISSN: | 2642-2077 |
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| Abstract | Tactile perception plays an important role in a variety of applications such as object detection and robotic grasping of objects. However, there is a lack of an intelligent object recognition algorithm based on tactile data with small storage space, fast computing speed, and high accuracy. Inspired by the involvement of dendrites of biological neurons in the computation of neural networks, we introduce dendritic computation to the task of object recognition based on tactile pressure data. Firstly, we design a tactile acquisition glove with a flexible film sensor array based on force-sensitive-resistors to obtain the pressure of the object. We conduct experiments by using this haptic acquisition device and obtain 24,500 tactile pressure images. Then we determine the optimal parameters of the DD model on the tactile pressure data by adjusting the number of DD modules. By using the DD model, we get information about the logical relationships between the tactile pressure image features. Finally, we achieve object recognition by capturing the pressure signal of the object and converting it into a pressure image. We train the model to achieve 99.84% accuracy, while the model achieves a storage space of 1.72MB and a recognition speed of 0.75ms, which outperforms existing algorithms in terms of real-time predictability, model storage space, and accuracy. This paper has significant implications for the future development of haptic applications in robotics. |
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| AbstractList | Tactile perception plays an important role in a variety of applications such as object detection and robotic grasping of objects. However, there is a lack of an intelligent object recognition algorithm based on tactile data with small storage space, fast computing speed, and high accuracy. Inspired by the involvement of dendrites of biological neurons in the computation of neural networks, we introduce dendritic computation to the task of object recognition based on tactile pressure data. Firstly, we design a tactile acquisition glove with a flexible film sensor array based on force-sensitive-resistors to obtain the pressure of the object. We conduct experiments by using this haptic acquisition device and obtain 24,500 tactile pressure images. Then we determine the optimal parameters of the DD model on the tactile pressure data by adjusting the number of DD modules. By using the DD model, we get information about the logical relationships between the tactile pressure image features. Finally, we achieve object recognition by capturing the pressure signal of the object and converting it into a pressure image. We train the model to achieve 99.84% accuracy, while the model achieves a storage space of 1.72MB and a recognition speed of 0.75ms, which outperforms existing algorithms in terms of real-time predictability, model storage space, and accuracy. This paper has significant implications for the future development of haptic applications in robotics. |
| Author | Gao, Tianshi Deng, Bin Liang, Haonan Jiangtao Luo, Tian Gao Wang, Jiang |
| Author_xml | – sequence: 1 givenname: Haonan surname: Liang fullname: Liang, Haonan email: lianghaonan@tju.edu.cn organization: School of Electrical and Information Engineering, Tianjin University,Tianjin,China – sequence: 2 givenname: Tianshi surname: Gao fullname: Gao, Tianshi email: tianshi@tju.edu.cn organization: School of Electrical and Information Engineering, Tianjin University,Tianjin,China – sequence: 3 givenname: Tian Gao surname: Jiangtao Luo fullname: Jiangtao Luo, Tian Gao email: luojiangtao@tju.edu.cn organization: School of Electrical and Information Engineering, Tianjin University,Tianjin,China – sequence: 4 givenname: Jiang surname: Wang fullname: Wang, Jiang email: jiangwang@tju.edu.cn organization: School of Electrical and Information Engineering, Tianjin University,Tianjin,China – sequence: 5 givenname: Bin surname: Deng fullname: Deng, Bin email: dengbin@tju.edu.cn organization: School of Electrical and Information Engineering, Tianjin University,Tianjin,China |
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| Snippet | Tactile perception plays an important role in a variety of applications such as object detection and robotic grasping of objects. However, there is a lack of... |
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| SubjectTerms | Data models Dendrite Net Dendrites (neurons) Feature extraction object recognition Predictive models Real-time systems Robot sensing systems robotic tactile pressure Training |
| Title | Fast and accurate object recognition based on tactile data by using Dendrite Net |
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