This work is licensed under the Creative Commons Attribution 4.0 International License.
Ali, M. H., Aizat, K., Yerkhan, K., Zhandos, T., & Anuar, O. (2018). Vision-based robot manipulator for industrial applications. Procedia computer science, 133, 205-212.Search in Google Scholar
Ren, Z., Fang, F., Yan, N., & Wu, Y. (2022). State of the art in defect detection based on machine vision. International Journal of Precision Engineering and Manufacturing-Green Technology, 9(2), 661-691.Search in Google Scholar
Yen, V. T., Nan, W. Y., & Van Cuong, P. (2019). Recurrent fuzzy wavelet neural networks based on robust adaptive sliding mode control for industrial robot manipulators. Neural Computing and Applications, 31(11), 6945-6958.Search in Google Scholar
Steger, C., Ulrich, M., & Wiedemann, C. (2018). Machine vision algorithms and applications. John Wiley & Sons.Search in Google Scholar
Urrea, C., & Pascal, J. (2018). Design, simulation, comparison and evaluation of parameter identification methods for an industrial robot. Computers & electrical engineering, 67, 791-806.Search in Google Scholar
Lins, R. G., de Araujo, P. R. M., & Corazzim, M. (2020). In-process machine vision monitoring of tool wear for Cyber-Physical Production Systems. Robotics and computer-integrated manufacturing, 61, 101859.Search in Google Scholar
Brito, T., Queiroz, J., Piardi, L., Fernandes, L. A., Lima, J., & Leitão, P. (2020). A machine learning approach for collaborative robot smart manufacturing inspection for quality control systems. Procedia Manufacturing, 51, 11-18.Search in Google Scholar
Fernández-Robles, L., Azzopardi, G., Alegre, E., & Petkov, N. (2017). Machine-vision-based identification of broken inserts in edge profile milling heads. Robotics and Computer-Integrated Manufacturing, 44, 276-283.Search in Google Scholar
Yao, B., Zhou, Z., Wang, L., Xu, W., Liu, Q., & Liu, A. (2018). Sensorless and adaptive admittance control of industrial robot in physical human− robot interaction. Robotics and Computer-Integrated Manufacturing, 51, 158-168.Search in Google Scholar
Zhou, L., Zhang, L., & Konz, N. (2022). Computer vision techniques in manufacturing. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 53(1), 105-117.Search in Google Scholar
Wang, T., Chen, B., Zhang, Z., Li, H., & Zhang, M. (2022). Applications of machine vision in agricultural robot navigation: A review. Computers and Electronics in Agriculture, 198, 107085.Search in Google Scholar
Quarta, D., Pogliani, M., Polino, M., Maggi, F., Zanchettin, A. M., & Zanero, S. (2017, May). An experimental security analysis of an industrial robot controller. In 2017 IEEE Symposium on Security and Privacy (SP) (pp. 268-286). IEEE.Search in Google Scholar
Javaid, M., Haleem, A., Singh, R. P., Rab, S., & Suman, R. (2022). Exploring impact and features of machine vision for progressive industry 4.0 culture. Sensors International, 3, 100132.Search in Google Scholar
Alonso, V., Dacal-Nieto, A., Barreto, L., Amaral, A., & Rivero, E. (2019). Industry 4.0 implications in machine vision metrology: an overview. Procedia manufacturing, 41, 359-366.Search in Google Scholar
Khang, A., Hajimahmud, V. A., Ali, R. N., Hahanov, V., & Avramovic, Z. (2024). Role of Machine Vision in Manufacturing and Industrial Revolution 4.0. In Machine Vision and Industrial Robotics in Manufacturing (pp. 1-13). CRC Press.Search in Google Scholar
Moru, D. K., & Borro, D. (2020). A machine vision algorithm for quality control inspection of gears. The International Journal of Advanced Manufacturing Technology, 106(1), 105-123.Search in Google Scholar
Penumuru, D. P., Muthuswamy, S., & Karumbu, P. (2020). Identification and classification of materials using machine vision and machine learning in the context of industry 4.0. Journal of Intelligent Manufacturing, 31(5), 1229-1241.Search in Google Scholar
Frank, D., Chhor, J., & Schmitt, R. (2017, December). Stereo-vision for autonomous industrial inspection robots. In 2017 IEEE International Conference on Robotics and Biomimetics (ROBIO) (pp. 2555-2561). IEEE.Search in Google Scholar
Benbarrad, T., Salhaoui, M., Kenitar, S. B., & Arioua, M. (2021). Intelligent machine vision model for defective product inspection based on machine learning. Journal of Sensor and Actuator Networks, 10(1), 7.Search in Google Scholar
Arents, J., & Greitans, M. (2022). Smart industrial robot control trends, challenges and opportunities within manufacturing. Applied Sciences, 12(2), 937.Search in Google Scholar
Evjemo, L. D., Gjerstad, T., Grøtli, E. I., & Sziebig, G. (2020). Trends in smart manufacturing: Role of humans and industrial robots in smart factories. Current Robotics Reports, 1, 35-41.Search in Google Scholar
Vick, A., & Krueger, J. (2018, June). Using OPC UA for distributed industrial robot control. In ISR 2018; 50th International Symposium on Robotics (pp. 1-6). VDE.Search in Google Scholar
Wang, Y. (2020). Robot algorithm based on neural network and intelligent predictive control. Journal of Ambient Intelligence and Humanized Computing, 11(12), 6155-6166.Search in Google Scholar
Qian, J., Zi, B., Wang, D., Ma, Y., & Zhang, D. (2017). The design and development of an omnidirectional mobile robot oriented to an intelligent manufacturing system. Sensors, 17(9), 2073.Search in Google Scholar
Qiangxian Huang,Tao Xiang,Zhihao Zhao,Kui Wu,Hongli Li,Rongjun Cheng... & Zhenying Cheng. (2024). Directional region-based feature point matching algorithm based on SURF. Journal of the Optical Society of America. A, Optics, image science, and vision(2),157-164.Search in Google Scholar
Han Luyang,Boese Markus,Gamm Bjoern & Tordoff Benjamin. (2021). A new beam alignment method in SEM based on parallax principle. Microscopy and Microanalysis(S1),1612-1613.Search in Google Scholar
Cui Yongbin. (2024). Application of cultural elements of dunhuang murals in landscape design based on mean shift algorithm extraction. Journal of Computational Methods in Sciences and Engineering(1),473-487.Search in Google Scholar
Yang Jinfeng,Que Huakun,Liu Wenjia & Xiao Jiang. (2024). A Monitoring Model for Abnormal Electricity Consumption Based on K-Means++ Clustering and Improved K-Nearest Neighbor Algorithm. Smart Grids and Sustainable Energy(2).Search in Google Scholar
N. Hanuman Reddy,Lathigara Amit,Aluvalu Rajanikanth & V. Uma Maheswari. (2024). Clustering based EO with MRF technique for effective load balancing in cloud computing. International Journal of Pervasive Computing and Communications(1),168-192.Search in Google Scholar
Wang Wentao & Tian Jun. (2022). An Improved Nonlinear Tuna Swarm Optimization Algorithm Based on Circle Chaos Map and Levy Flight Operator. Electronics(22),3678-3678.Search in Google Scholar