Integrating Tiny Machine Learning and Edge Computing for Real-Time Object Recognition in Industrial Robotic Arms
By integrating visual recognition technology and multi-object recognition into robotic arms, the flexibility and automation of the production process were improved in this study. By applying tiny machine learning (TinyML) and machine vision algorithms, we integrated edge computing devices to control...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-05-01
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| Series: | Engineering Proceedings |
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| Online Access: | https://www.mdpi.com/2673-4591/92/1/74 |
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| author | Nian-Ze Hu Bo-An Lin Yen-Yu Wu Hao-Lun Huang You-Xin Lin Chih-Chen Lin Po-Han Lu |
| author_facet | Nian-Ze Hu Bo-An Lin Yen-Yu Wu Hao-Lun Huang You-Xin Lin Chih-Chen Lin Po-Han Lu |
| author_sort | Nian-Ze Hu |
| collection | DOAJ |
| description | By integrating visual recognition technology and multi-object recognition into robotic arms, the flexibility and automation of the production process were improved in this study. By applying tiny machine learning (TinyML) and machine vision algorithms, we integrated edge computing devices to control the robotic arms and identified objects precisely on the production line, with ultra-low energy consumption. The developed system in this study included the SparkFun Edge development board and Raspberry Pi Camera Module 3, as edge devices for data processing, image recognition, and robotic arm control. By utilizing the Edge Impulse platform for data collection, model training, and optimization, edge devices and models for use in resource-limited environments were successfully generated. Using Edge Impulse’s automated toolchain, real-time image processing and object recognition were realized. The system achieved improved recognition accuracy and operational speed, demonstrating the potential of TinyML in enhancing the intelligence of robotic arms. MariaDB was chosen for data storage. Grafana was used to design a user-friendly web interface for real-time data monitoring and visualization and immediate data analysis and system monitoring. The developed system presented a success rate of 99% during actual operation. The feasibility of combining advanced image processing technology with robotic arms in intelligent manufacturing was verified in this study. The potential of integrating machine learning and automation technologies was also confirmed for the development of future manufacturing technologies. The model provides a technical reference and ideas for future factories that require high levels of automation and intelligence. |
| format | Article |
| id | doaj-art-e6717ef4e66447c2a664b41128d3777c |
| institution | Kabale University |
| issn | 2673-4591 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Engineering Proceedings |
| spelling | doaj-art-e6717ef4e66447c2a664b41128d3777c2025-08-20T03:24:39ZengMDPI AGEngineering Proceedings2673-45912025-05-019217410.3390/engproc2025092074Integrating Tiny Machine Learning and Edge Computing for Real-Time Object Recognition in Industrial Robotic ArmsNian-Ze Hu0Bo-An Lin1Yen-Yu Wu2Hao-Lun Huang3You-Xin Lin4Chih-Chen Lin5Po-Han Lu6Smart Machinery and Intelligent Manufacturing Research Center, National Formosa University, Yunlin 63201, TaiwanDepartment of Information Management, National Formosa University, Yunlin 63201, TaiwanDepartment of Information Management, National Formosa University, Yunlin 63201, TaiwanDepartment of Information Management, National Formosa University, Yunlin 63201, TaiwanDepartment of Information Management, National Formosa University, Yunlin 63201, TaiwanDepartment of Information Management, National Formosa University, Yunlin 63201, TaiwanDepartment of Information Management, National Formosa University, Yunlin 63201, TaiwanBy integrating visual recognition technology and multi-object recognition into robotic arms, the flexibility and automation of the production process were improved in this study. By applying tiny machine learning (TinyML) and machine vision algorithms, we integrated edge computing devices to control the robotic arms and identified objects precisely on the production line, with ultra-low energy consumption. The developed system in this study included the SparkFun Edge development board and Raspberry Pi Camera Module 3, as edge devices for data processing, image recognition, and robotic arm control. By utilizing the Edge Impulse platform for data collection, model training, and optimization, edge devices and models for use in resource-limited environments were successfully generated. Using Edge Impulse’s automated toolchain, real-time image processing and object recognition were realized. The system achieved improved recognition accuracy and operational speed, demonstrating the potential of TinyML in enhancing the intelligence of robotic arms. MariaDB was chosen for data storage. Grafana was used to design a user-friendly web interface for real-time data monitoring and visualization and immediate data analysis and system monitoring. The developed system presented a success rate of 99% during actual operation. The feasibility of combining advanced image processing technology with robotic arms in intelligent manufacturing was verified in this study. The potential of integrating machine learning and automation technologies was also confirmed for the development of future manufacturing technologies. The model provides a technical reference and ideas for future factories that require high levels of automation and intelligence.https://www.mdpi.com/2673-4591/92/1/74Industry 4.0TinyMLmulti-object recognitionedge computingreal-time visualizationEdge Impulse |
| spellingShingle | Nian-Ze Hu Bo-An Lin Yen-Yu Wu Hao-Lun Huang You-Xin Lin Chih-Chen Lin Po-Han Lu Integrating Tiny Machine Learning and Edge Computing for Real-Time Object Recognition in Industrial Robotic Arms Engineering Proceedings Industry 4.0 TinyML multi-object recognition edge computing real-time visualization Edge Impulse |
| title | Integrating Tiny Machine Learning and Edge Computing for Real-Time Object Recognition in Industrial Robotic Arms |
| title_full | Integrating Tiny Machine Learning and Edge Computing for Real-Time Object Recognition in Industrial Robotic Arms |
| title_fullStr | Integrating Tiny Machine Learning and Edge Computing for Real-Time Object Recognition in Industrial Robotic Arms |
| title_full_unstemmed | Integrating Tiny Machine Learning and Edge Computing for Real-Time Object Recognition in Industrial Robotic Arms |
| title_short | Integrating Tiny Machine Learning and Edge Computing for Real-Time Object Recognition in Industrial Robotic Arms |
| title_sort | integrating tiny machine learning and edge computing for real time object recognition in industrial robotic arms |
| topic | Industry 4.0 TinyML multi-object recognition edge computing real-time visualization Edge Impulse |
| url | https://www.mdpi.com/2673-4591/92/1/74 |
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