Research on the Application of Machine Vision in Industrial Robots

Main Article Content

Zeda Li
Chinesisch-Deutsche Technische Fakultät, Qingdao University of Science and Technology, Qingdao, China

DOI: https://doi.org/10.70267/mseac.2026164172

Keywords

machine vision, industrial robots, visual guidance, intelligent manufacturing, automation technology

Abstract

With the rapid development of intelligent manufacturing and Industry 4.0, industrial robots have been widely applied in modern manufacturing processes. However, traditional industrial robots often rely on pre - programmed instructions and fixed trajectories, which limit their flexibility and adaptability in complex environments. Machine vision technology enables industrial robots to perceive, identify, and analyse surrounding objects through image acquisition and processing. This paper systematically studies the application of machine vision in industrial robots. First, the basic concepts, components, working principles, and image processing technologies of machine vision are introduced. The structure and operating principles of industrial robot systems are then analysed. Furthermore, several typical applications of machine vision in industrial robots are discussed, including workpiece identification and positioning, visual guidance in welding and assembly, product quality inspection, and automatic sorting systems. In addition, the current challenges in machine vision technology, including environmental interference, target occlusion, high hardware costs, and real-time processing requirements, are analysed. Finally, future development trends are explored, including the integration of artificial intelligence, multi -sensor fusion, digital twins, and intelligent manufacturing systems. The study shows that machine vision plays an important role in improving the accuracy, efficiency, and automation level of industrial robots and will continue to promote the intelligent development of modern manufacturing.

Abstract 16 | PDF Downloads 12

References

  • [1] Zhong, R. Y., Xu, X., Klotz, E., & Newman, S. T. (2017). Intelligent manufacturing in the context of Industry 4.0: A review. Engineering, 3, 616–630. https://doi.org/10.1016/J.ENG.2017.05.015
  • [2] Zhou, H. A., Wolfschläger, D., Florides, C., Werheid, J., Behnen, H., Woltersmann, J.-H., Pinto, T. C., Kemmerling, M., Abdelrazeq, A., & Schmitt, R. H. (2026). Generative AI in industrial machine vision: A review. Journal of Intelligent Manufacturing, 37, 1447 –1470. https://doi.org/10.1007/s10845-025- 02604-6
  • [3] Pé rez, L., Rodrí guez, Í ., Rodrí guez, N., Usamentiaga, R., & Garcí a, D. F. (2016). Robot guidance using machine vision techniques in industrial environments: A comparative review. Sensors, 16(3), 335. https://doi.org/10.3390/s16030335
  • [4] Ghofrani, J., Kirschner, R., Roßburg, D., Reichelt, D., & Dimter, T. (2019). Machine vision in the context of robotics: A systematic literature review. arXiv:1905.03708.
  • [5] Yu, Y., Wang, C., Fu, Q., Kou, R., Huang, F., Yang, B., Yang, T., & Gao, M. (2023). Techniques and challenges of image segmentation: A review. Electronics, 12(5), 1199. https://doi.org/10.3390/electronics12051199
  • [6] Manakitsa, N., Maraslidis, G. S., Moysis, L., & Fragulis, G. F. (2024). A review of machine learning and deep learning for object detection, semantic segmentation, and human action recognition in machine and robotic vision. Technologies, 12(2), 15. https://doi.org/10.3390/technologies12020015
  • [7] Huang, Q., Guo, X., Wang, Y., Sun, H., & Yang, L. (2024). A survey of feature matching methods. IET Image Processing, 18(6), 1385–1410. https://doi.org/10.1049/ipr2.13032
  • [8] Orthey, A., Chamzas, C., & Kavraki, L. E. (2023). Sampling -based motion planning: A comparative review. arXiv. https://doi.org/10.48550/arXiv.2309.13119
  • [9] Dong, L., He, Z., Song, C., & Sun, C. (2021). A review of mobile robot motion planning methods: From classical motion planning workflows to reinforcement learning -based architectures. IEEE Transactions on Systems, Man, and Cybernetics: Systems
  • [10] Wang, H., Salunkhe, O., Quadrini, W., Lamkull, D., Ore, F., Despeisse, M., Fumagalli, L., Stahre, J., & Johansson, B. (2023). A systematic literature review of computer vision applications in robotized wire harness assembly. Procedia CIRP, 120, 1071–1076. https://doi.org/10.1016/j.procir.2023.09.127
  • [11]Werheid, J., Behnen, H., Woltersmann, J. H., He, S., Hamann, T., Abdelrazeq, A., & Schmitt, R. H. (2025). Machine vision in manufacturing SMEs: A review. Discover Applied Sciences, 7(5), 371. https://doi.org/10.1007/s42452-025-06923-4
  • [12] Wang, C., Lin, C.-Y., & Tomizuka, M. (2014). Statistical learning algorithms to compensate slow visual feedback for industrial robots. Journal of Dynamic Systems, Measurement, and Control, 136(2), 021025. https://doi.org/10.1115/1.4025988
  • [13] Robinson, N., Tidd, B., Campbell, D., & Kulić, D., & Corke, P. (2021). Robotic vision for human -robot interaction and collaboration: A survey and systematic review. Journal of the ACM, 37(4), Article 111. https://doi.org/10.1145/1122445.1122456
  • [14] Faqeer, H. A., & Khajavi, S. H. (2025). Digital twin and computer vision combination for manufacturing and operations: A systematic literature review. Applied Sciences , 15(18), 10157. https://doi.org/10.3390/app151810157

Article Sidebar

PDF
Published
Apr 15, 2026
Conference Proceedings Volume
Vol. 20 (2026): Proceedings of 2026 International Conference on Materials Science, Energy Engineering and Automation Control (IC-MSEAC 2026)
Section
Articles
Creative Commons License

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

How to Cite

Li, Zeda. “Research on the Application of Machine Vision in Industrial Robots”. Exploring Science Academic Conference Series, vol. 20, Apr. 2026, pp. 164-72, https://doi.org/10.70267/mseac.2026164172.