Vision-Guided Robotic Assembly Using Deep Neural Networks

  • Authors

    • Ole-Johan Dahl Professor, University of Oslo, Norway. Author
    • Kristen Nygaard Professor, University of Oslo, Norway. Author

    DOI:

    https://doi.org/10.67228/30715725/IJIARE-2021PII5T9I

    Published 08-03-2021

  • Vision-Guided Robotics, Robotic Assembly, Deep Neural Networks, Convolutional Neural Networks, Computer Vision, Intelligent Manufacturing, Industrial Automation, Object Recognition, Robot Manipulation, Industry 4.0

    Issue

    Vol. 4 No. 2 (2021)

    Section

    Articles

    How to Cite

    [1]
    O.-J. Dahl and K. Nygaard, “Vision-Guided Robotic Assembly Using Deep Neural Networks”, IJIARE, vol. 4, no. 2, pp. 01–15, Aug. 2021, doi: 10.67228/30715725/IJIARE-2021PII5T9I.

  • Abstract

    Intelligent manufacturing is transforming traditional robotic assembly into adaptive, autonomous, and data-driven production systems. Conventional robotic assembly relies on pre-programmed trajectories, structured environments, and rule-based vision systems, limiting flexibility in handling complex components and uncertain operating conditions. This paper proposes a deep neural network (DNN)-based vision-guided robotic assembly framework that integrates computer vision, intelligent decision-making, and real-time robotic control. The framework consists of four modules: vision acquisition, deep feature learning, assembly intelligence, and robotic execution. Industrial cameras and depth sensors capture visual data, while convolutional neural networks (CNNs) perform object recognition and pose estimation. Extracted visual features are combined with motion planning to generate optimized assembly trajectories. Mathematical models for feature extraction, neural network optimization, and robotic coordinate transformation enhance system accuracy and reliability. Performance is evaluated using recognition accuracy, assembly precision, processing speed, adaptability, and operational efficiency. Experimental results demonstrate that the proposed framework outperforms conventional image processing and machine learning approaches, enabling accurate assembly of irregular components under uncertain conditions. The proposed approach enhances perception, autonomous decision-making, and intelligent adaptation, supporting flexible automation and next-generation smart manufacturing in Industry 4.0 environments.

  • References

    [1] D. G. Lowe, “Distinctive Image Features from Scale-Invariant Keypoints,” International Journal of Computer Vision, vol. 60, no. 2, pp. 91–110, 2004.

    [2] H. Bay, A. Ess, T. Tuytelaars, and L. Van Gool, “Speeded-Up Robust Features (SURF),” Computer Vision and Image Understanding, vol. 110, no. 3, pp. 346–359, 2008.

    [3] N. Dalal and B. Triggs, “Histograms of Oriented Gradients for Human Detection,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2005, pp. 886–893.

    [4] R. Hartley and A. Zisserman, Multiple View Geometry in Computer Vision, 2nd ed. Cambridge, U.K.: Cambridge University Press, 2004.

    [5] S. K. Nayar, S. A. Nene, and H. Murase, “Subspace Methods for Robot Vision,” IEEE Transactions on Robotics and Automation, vol. 12, no. 5, pp. 750–758, 1996.

    [6] C. Cortes and V. Vapnik, “Support-Vector Networks,” Machine Learning, vol. 20, no. 3, pp. 273–297, 1995.

    [7] T. M. Mitchell, Machine Learning. New York, NY, USA: McGraw-Hill, 1997.

    [8] L. Breiman, “Random Forests,” Machine Learning, vol. 45, no. 1, pp. 5–32, 2001.

    [9] Y. LeCun, Y. Bengio, and G. Hinton, “Deep Learning,” Nature, vol. 521, pp. 436–444, 2015.

    [10] A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet Classification with Deep Convolutional Neural Networks,” in Advances in Neural Information Processing Systems (NeurIPS), 2012, pp. 1097–1105.

    [11] K. Simonyan and A. Zisserman, “Very Deep Convolutional Networks for Large-Scale Image Recognition,” in International Conference on Learning Representations (ICLR), 2015.

    [12] K. He, X. Zhang, S. Ren, and J. Sun, “Deep Residual Learning for Image Recognition,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 770–778.

    [13] S. Ren, K. He, R. Girshick, and J. Sun, “Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 39, no. 6, pp. 1137–1149, 2017.

    [14] J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, “You Only Look Once: Unified, Real-Time Object Detection,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 779–788.

    [15] J. Kober, J. A. Bagnell, and J. Peters, “Reinforcement Learning in Robotics: A Survey,” International Journal of Robotics Research, vol. 32, no. 11, pp. 1238–1274, 2013.

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