Search

Research on Object Sorting Technology of Collaborative Robots Based on Machine Vision

Author(s)

Yingkai Ma, Liye Zhang

Affiliation(s)

School of Computer Science and Technology, Shandong University of Technology, Zibo, Shandong, China

Abstract

Collaborative robot object sorting based on machine vision integrates multiple technologies and is highly valued in industrial production and logistics automation for improving efficiency, reducing labor costs and enabling precise operations. This thesis focuses on this technology and achieves key results: dual-depth perception cameras for object coordinate positioning, inter-thread communication between the robotic arm and camera main control unit, HTTP communication between the main controller and gripper, multi-threaded operation to alleviate program congestion, and real-time system status monitoring via flag bits. These achievements realize core functions and modularize the project through standardized communication interfaces, facilitating hardware replacement.

Keywords

Machine Vision; Inter-Thread Communication; Target Detection; Mechanical Arm

References

[1]Chen, J. F., & Zheng, M. H. (2022). A review of robot manipulation behavior research based on deep reinforcement learning. Robot, 44 [2]Zhu, C. L. (1987). Development, methods and applications of machine vision. Aerospace Precision Mechanical Engineering, (1), 62-67. [3]Craig, J. J. (2018). Introduction to Robotics (4th ed.). Beijing: China Machine Press. [4]Xu, H. W., & Gao, J. L. (2022). ROS2 Robot Programming in Practice: Based on Modern C++ and Python3. Beijing: China Machine Press. [5]Ding, H. R., Deng, E. P., Zhou, W. F., Yu, Z. M., Liu, T. H., & Li, S. Y. (2024). Research on dynamic calibration method of dual-arm collaborative robot based on binocular stereo vision. Robot Technology and Application, (1), 22-27. [6]Cheng, P. F., Liu, M. T., & Sun, K. (2024). Research on dynamic target grasping control method of food collaborative robot. Food and Machinery, 40(1), 95-100. [7]Shao, X. J., Huang, J. C., Zhao, L. C., & Jin, M. S. (2013). Research on multi-stepping motor control based on new acceleration and deceleration curve. Automation & Instrumentation, 28(4), 53-56. [8]Ge, S. H. (2024). Research on key technologies of test tube sorting robot for emergency and clinical scenarios. Zhejiang University of Science and Technology. [9]Li, C. M. (2017). Research on target recognition and grasping positioning based on machine vision and deep learning. North University of China, Shanxi Province. [10]Liu, C. (2023). Discussion on sorting technology of industrial robots based on machine vision. Popular Standardization, 23(22), 42-44. [11]Zhang, P. W., & Li, X. W. (2023). Multi-parameter mining control technology for sorting robot grasping process under contact state perception. Machine Design and Research, 39(5), 24-28. [12]Szeliski, Richard. Computer Vision: Algorithms and Applications. Springer, 2010. [13]Forsyth, David A., and Ponce, Jean. Computer Vision: A Modern Approach. Prentice Hall, 2002.Szeliski, Richard. Computer Vision: Algorithms and Applications. Springer, 2010. [14]Hartley, Richard, and Zisserman, Andrew. Multiple View Geometry in Computer Vision. Cambridge University Press, 2004. [15]Bradski, Gary, and Kaehler, Adrian. Learning OpenCV: Computer Vision in C++ with the OpenCV Library. O'Reilly Media, 2008.Bradski, Gary, and Kaehler, Adrian. Learning OpenCV: Computer Vision in C++ with the OpenCV Library. O'Reilly Media, 2008. [16]Chaoyue Sun, Yajun Chen, Xiaoyang Qiu, Rongzhen Li, Longxiang You. MRD-YOLO: A Multispectral Object Detection Algorithm for Complex Road Scenes. Sensors. Volume 24, Issue 10.