Pattern Recognition on Automated Guided Vehicles Two Wheel Drive (AGV 2WD) Robot for Location Detection Based on Raspberry Pi 4 Model B


  • Florentinus Budi Setiawan Soegijapranata Chatolic University Semarang, Indonesia
  • Ilyas Muntaha Soegijapranata Chatolic University Semarang, Indonesia
  • Leonardus Heru Pratomo Soegijapranata Chatolic University Semarang, Indonesia
  • Slamet Riyadi Soegijapranata Chatolic University Semarang, Indonesia




robot, AGV, movement system, computer vision, raspberry pi insert, artificial intelligence


AGV (Automated Guided Vehicle) equipped with artificial intelligence (AI) is anticipated to boost Indonesia's industrial development. The little computer used to generate this robot's artificial intelligence used mechanical gears similar to those found in an eight-wheeled, two-wheel-drive car (2WD). This article outlines and demonstrates the usage of an AGV-controlled approach to determine a place inside a building by detecting text in different locations throughout the building. The current technique employs the programming languages Python and OpenCV. Optical Character Recognition (OCR) has been tweaked or enhanced for usage with OpenCV. Multiple texts are read using OCR as the principal technique. In this instance, OCR functions at many stages of the process, in addition to being employed for exploring letters and words, word translation, character classification, linguistic analysis, and adaptive character classification. The output text from the system's document processing procedure will likely contain the location or even the position of an AGV robot once the process has concluded. This text is produced from the text that was previously submitted using the camera function. After a thorough search, the AGV robot will go to the next area before returning to its starting point. The method above can be implemented on the AGV lab's hardware, which has a solid basis

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F. Zhao, X. Xie, and M. Roach, “Computer Vision Techniques for Transcatheter Intervention,” IEEE J. Transl. Eng. Heal. Med., vol. 3, no. April, 2015, doi: 10.1109/JTEHM.2015.2446988.

G. Vaidya, K. Vaidya, and K. Bhosale, “Text recognition system for visually impaired using portable camera,” 2020 Int. Conf. Converg. to Digit. World - Quo Vadis, ICCDW 2020, no. Iccdw, pp. 1–4, 2020, doi: 10.1109/ICCDW45521.2020.9318706

H. Song, “The Application of Computer Vision in Responding tto the Emergencies of Autonomous Driving,” Proc. - 2020 Int. Conf. Comput. Vision, Image Deep Learn. CVIDL 2020, no. Cvidl, pp. 1–5, 2020, doi: 10.1109/CVIDL51233.2020.00008.

I. Ahmad, I. Moon, and S. J. Shin, “Color-to-grayscale algorithms effect on edge detection - A comparative study,” Int. Conf. Electron. Inf. Commun. ICEIC 2018, vol. 2018-January, pp. 1–4, 2018, doi: 10.23919/ELINFOCOM.2018.8330719

I. Draganjac and S. Bogdan, “Decentralized Control of Multi-AGV Systems in,” IEEE Trans. Autom. Sci. Eng., vol. 13, no. 4, pp. 1–15, 2016, [Online]. Available:

J. Zhang and X. Liu-Henke, “Model-based design of the vehicle dynamics control for an omnidirectional automated guided vehicle (AGV),” 15th Int. Conf. Mechatron. Syst. Mater. MSM 2020, pp. 1–6, 2020, doi: 10.1109/MSM49833.2020.9202248.

K. Afsari and M. Saadeh, “Artificial Intelligence Platform for Low-Cost Robotics,” 2020 3rd Int. Conf. Signal Process. Inf. Secur. ICSPIS 2020, pp. 4–7, 2020, doi: 10.1109/ICSPIS51252.2020.9340156.

S. Vechet, J. Krejsa, and K. S. Chen, “AGVs mission control support in smart factories by decision networks,” Proc. 2020 19th Int. Conf. Mechatronics - Mechatronika, ME 2020, no. 1, pp. 3–6, 2020, doi: 10.1109/ME49197.2020.9286465.

T. T. H. Nguyen, A. Jatowt, M. Coustaty, N. Van Nguyen, and A. Doucet, “Post-OCR error detection by generating plausible candidates,” Proc. Int. Conf. Doc. Anal. Recognition, ICDAR, pp. 876–881, 2019, doi: 10.1109/ICDAR.2019.00145

X. Li and Y. Shi, “Computer vision imaging based on artificial intelligence,” Proc. - 2018 Int. Conf. Virtual Real. Intell. Syst. ICVRIS 2018, pp. 22–25, 2018, doi: 10.1109/ICVRIS.2018.00014

X. Zhou, T. Chen, and Y. Zhang, “Research on Intelligent AGV Control System,” Proc. 2018 Chinese Autom. Congr. CAC 2018, no. 1, pp. 58–61, 2019, doi: 10.1109/CAC.2018.8623384.

Y. Li and Y. Zhang, “Application research of computer vision technology in automation,” Proc. - 2020 Int. Conf. Comput. Inf. Big Data Appl. CIBDA 2020, pp. 374–377, 2020, doi: 10.1109/CIBDA50819.2020.00090.

Y. M. Su, H. W. Peng, K. W. Huang, and C. S. Yang, “Image processing technology for text recognition,” Proc. - 2019 Int. Conf. Technol. Appl. Artif. Intell. TAAI 2019, pp. 1–5, 2019, doi: 10.1109/TAAI48200.2019.8959877.

Y. Ma, B. Jiang, and V. Cocquempot, “Modeling and Adaptive Fault Compensation for Two Physically Linked 2WD Mobile Robots,” IEEE/ASME Trans. Mechatronics, vol. 26, no. 2, pp. 1161–1171, 2021, doi: 10.1109/TMECH.2021.3052900.

Z. Zhong, L. Sun, and Q. Huo, “Improved Localization Accuracy by LocNet for Faster R-CNN Based Text Detection,” Proc. Int. Conf. Doc. Anal. Recognition, ICDAR, vol. 1, pp. 923–928, 2017, doi: 10.1109/ICDAR.2017.155

F. B. Setiawan, O. J. Aldo Wijaya, L. H. Pratomo, and S. Riyadi, “Sistem Navigasi Automated Guided Vehicle Berbasis Computer Vision dan Implementasi pada Raspberry Pi,” J. Rekayasa Elektr., vol. 17, no. 1, pp. 7–14, 2021, doi: 10.17529/jre.v17i1.18087

. Setiawan. B. S, Kurnianingsih .F. A, Riyadi. S, & Pratomo. L. H, “Pattern Recognition untuk Deteksi Posisi pada AGV Berbasis Raspberry Pi,” J. Nas. Tek. Elektro dan Teknol. Inf., vol. 10, no. 1, pp. 49–56, 2021, doi: 10.22146/jnteti.v10i1.738


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How to Cite

Setiawan, F. B. ., Muntaha, I. ., Pratomo, L. H. ., & Riyadi, S. . (2023). Pattern Recognition on Automated Guided Vehicles Two Wheel Drive (AGV 2WD) Robot for Location Detection Based on Raspberry Pi 4 Model B. Sinkron : Jurnal Dan Penelitian Teknik Informatika, 8(1), 338-347.