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The visual-based robotic language for industry 4.0 applications: Robotic U Language

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Abstract

In this article, a visual-based robotic language is proposed for the management of mobile robots, which is an important element of the new industrial revolution. Efficient management of artificial intelligence robots within industry 4.0 applications is an important problem. Thanks to the use of the proposed language, it is aimed to increase the energy efficiency of mobile robots. The proposed visual-based robot language has a two-dimensional structure, but it is not actually an image by its nature. It is a visual-based code designed to provide a solution to the blurring problem in image-based visual coding. The proposed method basically consists of rods and is a coding based on arranging rods of varying lengths in a two-dimensional plane. According to the presented experimental studies, the robustness of the proposed visual-based robotic language against blurring has been demonstrated.

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All data used during this study are included in this published article.

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References

  1. Muhuri, P.K., Shukla, A.K., Abraham, A.: Industry 4.0: a bibliometric analysis and detailed overview. Eng. Appl. Artif. Intell. 78, 218–235 (2019)

    Article  Google Scholar 

  2. Ottoni, A., Novo, M., Costa, D.: Deep learning for vision systems in construction 4.0: a systematic review. Signal Image Video Process. 17, 1821–1829 (2023). https://doi.org/10.1007/s11760-022-02393-y

    Article  Google Scholar 

  3. Antonino, P.O., Capilla, R., Pelliccione, P., Schnicke, F., Espen, D., Kuhn, T., Schmid, K.: A Quality 4.0 Model for architecting industry 4.0 systems. Adv. Eng. Inf. 54, 101801 (2022)

    Article  Google Scholar 

  4. Jan, Z., Ahamed, F., Mayer, W., Patel, N., Grossmann, G., Stumptner, M., Kuusk, A.: Artificial intelligence for industry 4.0: systematic review of applications, challenges, and opportunities. Exp. Syst. Appl. 216, 119456 (2023)

    Article  Google Scholar 

  5. Cao, Q., Zanni-Merk, C., Samet, A., Reich, C., et al.: KSPMI: a knowledge-based system for predictive maintenance in industry 4.0. Robot. Comput. Integr. Manuf. 74, 102281 (2022)

    Article  Google Scholar 

  6. López, A., Casquero, O., Estévez, E., Armentia, A., Orive, D., Marcos, M.: An industrial agent-based customizable platform for I4.0 manufacturing systems. Comput. Indus. 146, 103859 (2023)

    Article  Google Scholar 

  7. Farooq, M.U., Eizad, A., Bae, H.-K.: Power solutions for autonomous mobile robots: a survey. Robot. Auton. Syst. 159, 104285 (2023)

    Article  Google Scholar 

  8. Cebollada, S., Payá, L., Flores, M., Peidró, A., Reinoso, O.: A state-of-the-art review on mobile robotics tasks using artificial intelligence and visual data. Expert Syst. Appl. 167, 114195 (2021)

    Article  Google Scholar 

  9. Zamora-Hernández, M.-A., Castro-Vargas, J.A., Azorin-Lopez, J., Garcia-Rodriguez, J.: Deep learning-based visual control assistant for assembly in Industry 4.0. Comput. Indus. 131, 103485 (2021)

    Article  Google Scholar 

  10. Lei, T., Rong, Y., Wang, H., Huang, Y., Li, M.: A review of vision-aided robotic welding. Comput. Ind. 123, 103326 (2020)

    Article  Google Scholar 

  11. Xiao, R., Xu, Y., Hou, Z., Xu, F., Zhang, H., Chen, S.: A novel visual guidance framework for robotic welding based on binocular cooperation. Robot. Comput. Integr. Manuf. 78, 102393 (2022)

    Article  Google Scholar 

  12. Kazerouni, I.A., Fitzgerald, L., Dooly, G., Toal, D.: A survey of state-of-the-art on visual SLAM. Expert Syst. Appl. 205, 117734 (2022)

    Article  Google Scholar 

  13. D’Avella, S., Avizzano, C.A., Tripicchio, P.: ROS-industrial based robotic cell for industry 4.0: Eye-in-hand stereo camera and visual servoing for flexible, fast, and accurate picking and hooking in the production line. Robot Comput Integr Manuf 80, 102453 (2023)

    Article  Google Scholar 

  14. Arishi, A., Krishnan, K., Arishi, M.: Machine learning approach for truck-drones based last-mile delivery in the era of industry 4.0. Eng. Appl. Artif. Intell. 116, 05439 (2022)

    Article  Google Scholar 

  15. Hara, M., Watabe, M., Nojiri, T., Nagaya, T., Uchiyama, Y.: Optically readable two-dimensional code and method and apparatus using the same. US Patent 5, 726435 (1998)

    Google Scholar 

  16. Zheng, Z., Yu, Y., Chen, R., Huang, H., Zhao, H., Lu, X.: Localization method based on multi-QR codes for mobile robots. In: 2022 IEEE International Conference on Advances in Electrical Engineering and Computer Applications (AEECA), (2022)

  17. Rohmann, C., Lenkowski, J., Bachem, H., Lichte, B.: Highly accurate positioning method for car-like robots utilizing a monocular camera and QR code tracking. In: 2022 IEEE International Symposium on Robotic and Sensors Environments (ROSE), (2022)

  18. Cavanini, L., Cimini, G., Ferracuti, F., Freddi, A., Ippoliti, G., Monteriù, A., Verdini, F.: A QR-code localization system for mobile robots: application to smart wheelchairs. In: 2017 European Conference on Mobile Robots (ECMR), (2017)

  19. Sakarya, U.: Endüstri 4.0 Uygulamaları İçin Görsel Temelli Robot Dili. Patent: Türk Patent 2022/014689, (2022)

  20. Matlab, 2023. Available: https://www.mathworks.com/. (Online)

  21. QR Code Generator, Accessed 07 Apr 2023 from https://www.qr-code-generator.com/. (Online)

  22. QR Code Reader, Accessed 07 Apr 2023 from https://apps.apple.com/in/app/qr-reader-for-iphone/id368494609. (Online)

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Funding

No funding was received for conducting this study.

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Authors and Affiliations

  1. Department of Aviation Electrics and Electronics, Faculty of Applied Sciences, Yildiz Technical University, Istanbul, Türkiye

    Ufuk Sakarya

  2. Department of Electronics and Communication Engineering, Faculty of Electrical and Electronics, Yildiz Technical University, Istanbul, Türkiye

    Ufuk Sakarya

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  1. Ufuk Sakarya
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All the stdudies for creating this paper is done by Ufuk SAKARYA.

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Correspondence to Ufuk Sakarya.

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The author declares the following competing interest: Ufuk SAKARYA (As Inventor) (Yildız Teknik Üniversitesi As Applicant) has patent #2022/014689 pending to Türk Patent.

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Sakarya, U. The visual-based robotic language for industry 4.0 applications: Robotic U Language. SIViP 18, 91–98 (2024). https://doi.org/10.1007/s11760-023-02713-w

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  • DOI: https://doi.org/10.1007/s11760-023-02713-w

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