Using Contouring Algorithms to Select Objects in the Robots’ Workspace
PDF
DOI

Keywords

Industry 5.0, Сomputer Vision Systems, Mobile Robots, Work zone.

How to Cite

Vladyslav Yevsieiev, Svitlana Maksymova, & Nataliia Demska. (2024). Using Contouring Algorithms to Select Objects in the Robots’ Workspace. TECHNICAL SCIENCE RESEARCH IN UZBEKISTAN, 2(2), 32–42. Retrieved from https://universalpublishings.com/index.php/tsru/article/view/4204

Abstract

This paper explores the application of contouring algorithms to accurately highlight objects in the robots’ workspace. We present a mathematical description of the developed algorithm and a Python program that implements it in the PyCharm 2022.2.3 (Professional Edition) environment. Experiments carried out using this algorithm focused on outlining a matchbox while changing the pixel intensity threshold. The results obtained confirm the effectiveness of the method and highlight its potential for optimizing the processes of robotic perception of the environment and interaction with objects.

PDF
DOI

References

Abu-Jassar, A. T., Al-Sharo, Y. M., Lyashenko, V., & Sotnik, S. (2021). Some Features of Classifiers Implementation for Object Recognition in Specialized Computer systems. TEM Journal: Technology, Education, Management, Informatics, 10(4), 1645-1654.

Baker, J. H., Laariedh, F., Ahmad, M. A., Lyashenko, V., Sotnik, S., & Mustafa, S. K. (2021). Some interesting features of semantic model in Robotic Science. SSRG International Journal of Engineering Trends and Technology, 69(7), 38-44.

Rabotiahov, A., Kobylin, O., Dudar, Z., & Lyashenko, V. (2018, February). Bionic image segmentation of cytology samples method. In 2018 14th International Conference on Advanced Trends in Radioelecrtronics, Telecommunications and Computer Engineering (TCSET) (pp. 665-670). IEEE.

Гиренко, А. В., Ляшенко, В. В., Машталир, В. П., & Путятин, Е. П. (1996). Методы корреляционного обнаружения объектов. Харьков: АО “БизнесИнформ, 112.

Al-Sharo, Y. M., Abu-Jassar, A. T., Sotnik, S., & Lyashenko, V. (2021). Neural Networks As A Tool For Pattern Recognition of Fasteners. International Journal of Engineering Trends and Technology, 69(10), 151-160.

Matarneh R., & et al. (2017). Building Robot Voice Control Training Methodology Using Artificial Neural Net. International Journal of Civil Engineering and Technology, 8(10), 523–532.

Akopov, M., & et al. (2023). Choosing a Camera for 3D Mapping. Journal of Universal Science Research, 1(11), 28-38.

Abu-Jassar, A., & Maksymova, S. (2023). Obstacle Avoidance Sensors: A Brief Overview. Multidisciplinary Journal of Science and Technology, 3(5), 4-10.

Yevsieiev, V., & et al. (2022). Development of an Algorithm for ESP32-Cam Operation in HTTP Server Mode for Streaming Video. Collection of scientific papers «ΛΌГOΣ», Paris, 177-179.

Maksymova S., & et al. (2017). Software for Voice Control Robot: Example of Implementation. Open Access Library Journal, 4, 1-12.

Matarneh R., & et al. (2017). Speech Recognition Systems: A Comparative Review. Journal of Computer Engineering (IOSR-JCE), 19(5), 71–79.

Nevliudov, I., & et al. (2023). Mobile Robot Navigation System Based on Ultrasonic Sensors. In 2023 IEEE XXVIII International Seminar/Workshop on Direct and Inverse Problems of Electromagnetic and Acoustic Wave Theory (DIPED), IEEE, 1, 247-251.

Matarneh R., & et al. (2018). Voice Control for Flexible Medicine Robot. International Journal of Computer Trends and Technology (IJCTT), 55(1),

-5.

Nevliudov, I., & et al. (2023). Development of a Mobile Robot Prototype with an Interactive Control System. Системи управління, навігації та зв’язку. Збірник наукових праць, 3(73), 128-133.

Matarneh, R., Tvoroshenko, I., & Lyashenko, V. (2019). Improving Fuzzy Network Models For the Analysis of Dynamic Interacting Processes in the State Space. International Journal of Recent Technology and Engineering, 8(4), 1687-1693.

Babker, A. M., Altoum, A. E. A., Tvoroshenko, I., & Lyashenko, V. (2019). Information technologies of the processing of the spaces of the states of a complex biophysical object in the intellectual medical system health. International Journal of Advanced Trends in Computer Science and Engineering, 8(6), 3221-3227.

Lyashenko, V., Ahmad, M. A., Sotnik, S., Deineko, Z., & Khan, A. (2018). Defects of communication pipes from plastic in modern civil engineering. International Journal of Mechanical and Production Engineering Research and Development, 8(1), 253-262.

Lyashenko, V. V., Babker, A. M. A. A., & Kobylin, O. A. (2016). The methodology of wavelet analysis as a tool for cytology preparations image processing. Cukurova Medical Journal, 41(3), 453-463.

Lyashenko, V., Kobylin, O., & Ahmad, M. A. (2014). General methodology for implementation of image normalization procedure using its wavelet transform. International Journal of Science and Research (IJSR), 3(11), 2870-2877.

Kobylin, O., & Lyashenko, V. (2014). Comparison of standard image edge detection techniques and of method based on wavelet transform. International Journal, 2(8), 572-580.

Tvoroshenko, I., Ahmad, M. A., Mustafa, S. K., Lyashenko, V., & Alharbi, A. R. (2020). Modification of models intensive development ontologies by fuzzy logic. International Journal of Emerging Trends in Engineering Research, 8(3), 939-944.

Sotnik, S., Mustafa, S. K., Ahmad, M. A., Lyashenko, V., & Zeleniy, O. (2020). Some features of route planning as the basis in a mobile robot. International Journal of Emerging Trends in Engineering Research, 8(5), 2074-2079.

Lyubchenko, V., & et al.. (2016). Digital image processing techniques for detection and diagnosis of fish diseases. International Journal of Advanced Research in Computer Science and Software Engineering, 6(7), 79-83.

Lyashenko, V., Matarneh, R., & Kobylin, O. (2016). Contrast modification as a tool to study the structure of blood components. Journal of Environmental Science, Computer Science and Engineering & Technology, 5(3), 150-160.

Uchqun o‘g‘li, B. S., Valentin, L., & Vyacheslav, L. (2023). Image Processing Techniques as a Tool for the Analysis of Liver Diseases. Journal of Universal Science Research, 1(8), 223-233.

Yuan, J., & et al. (2020). An underwater image vision enhancement algorithm based on contour bougie morphology. IEEE Transactions on Geoscience and Remote Sensing, 59(10), 8117-8128.

Lin, X., & et al. (2020). Intelligent detection of edge inconsistency for mechanical workpiece by machine vision with deep learning and variable geometry model. Applied Intelligence, 50, 2105-2119.

Lu, D., & Yan, L. (2021). Face detection and recognition algorithm in digital image based on computer vision sensor. Journal of Sensors, 2021, 1-16.

Zheng, S. (2020). Design of intelligent manufacturing product identification and detection system based on machine vision. In Cyber Security Intelligence and Analytics: Proceedings of the 2020 International Conference on Cyber Security Intelligence and Analytics (CSIA 2020), Springer International Publishing, Volume 1 258-265).

Sowah, R. R., & et al. (2021). An intelligent instrument reader: using computer vision and machine learning to automate meter reading. IEEE Industry Applications Magazine, 27(4), 45-56.

Yang, L., & et al. (2020). Diagram image retrieval and analysis: Challenges and opportunities. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 180-181.

Khort, D., & et al. (2020). Computer vision system for recognizing the coordinates location and ripeness of strawberries. In International Conference on Data Stream Mining and Processing, Cham: Springer International Publishing, 334-343.

Qi, S., & et al. (2020). A review on industrial surface defect detection based on deep learning technology. In Proceedings of the 2020 3rd International Conference on Machine Learning and Machine Intelligence, 24-30.

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.