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https://dspace.ncfu.ru/handle/123456789/27484| Title: | Robotic Manipulator Software and Hardware Development Using Recognition Algorithms |
| Authors: | Martirosyan, K. V. Мартиросян, К. В. |
| Keywords: | Arduino;Statistics;Automated sorting system;Computer vision;Data collection and analysis;Defect detection;Image classification;Neural networks;Product quality control;Robotics in manufacturing |
| Issue Date: | 2024 |
| Citation: | Borovkov, G.S., Martirosyan, K.V., Protasov, Y.A. Robotic Manipulator Software and Hardware Development Using Recognition Algorithms // Proceedings of the 2024 Conference of Young Researchers in Electrical and Electronic Engineering, ElCon 2024. - 2024. - pp. 335-338. - DOI: 10.1109/ElCon61730.2024.10468466 |
| Series/Report no.: | Proceedings of the 2024 Conference of Young Researchers in Electrical and Electronic Engineering, ElCon 2024 |
| Abstract: | The enterprises digitalization determines the development trends of the industrial sector. There is a need to reduce the percent of human participation in processes associated with conveyor production, which requires the high-tech solutions integration. Due to the lack of quality control on the assembly line, enterprises produce a large percentage of defective products. The article discusses a possible solution to the problem of image processing for monitoring defects in products moving along a conveyor belt. The use of neural network technologies allows us to identify defects in production in real time, and the use of a robotic arm allows us to immediately remove such products from the assembly line. As a result of the research, it is planned to implement a software and hardware complex of a robotic manipulator using computer vision technologies that can distinguish objects and mechanically influence them. |
| URI: | https://dspace.ncfu.ru/handle/123456789/27484 |
| Appears in Collections: | Статьи, проиндексированные в SCOPUS, WOS |
Files in This Item:
| File | Size | Format | |
|---|---|---|---|
| scopusresults 3069 .pdf Restricted Access | 132.72 kB | Adobe PDF | View/Open |
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