Intelligent Robotic Pick-and-Sort Systems for Dynamic Production

  • Authors

    • Dr. Nandhini Ravi Assistant Professor, VIT University, India. Author

    DOI:

    https://doi.org/10.67228/30715725/IJIARE-2021PII4S6X

    Published 10-04-2021

  • Intelligent Robotics, Pick-and-Sort System, Dynamic Production Lines, Industry 4.0, Artificial Intelligence, Machine Learning, Computer Vision, Deep Learning, Industrial Internet of Things (IIoT), Robotic Manipulators, Motion Planning, Smart Manufacturing, Automation

    Issue

    Vol. 4 No. 2 (2021)

    Section

    Articles

    How to Cite

    [1]
    N. Ravi, “Intelligent Robotic Pick-and-Sort Systems for Dynamic Production”, IJIARE, vol. 4, no. 2, pp. 01–15, Oct. 2021, doi: 10.67228/30715725/IJIARE-2021PII4S6X.

  • Abstract

    Industry 4.0 has transformed conventional manufacturing into intelligent, automated, and connected production environments. Intelligent robotic pick-and-sort systems improve productivity, flexibility, product quality, and operational efficiency by overcoming the limitations of traditional rule-based automation. This paper presents an AI-enabled framework integrating computer vision, deep learning, robotic manipulation, edge computing, and the Industrial Internet of Things (IIoT) for dynamic manufacturing applications. Convolutional Neural Networks (CNNs) provide accurate object detection and classification, while intelligent motion planning and reinforcement learning optimize robotic grasping and movement. Sensor fusion, edge computing, and IIoT connectivity enable real-time monitoring, low-latency decision-making, and predictive maintenance, improving system reliability and reducing downtime. Performance is evaluated using object detection accuracy, sorting accuracy, processing time, throughput, energy efficiency, and overall system reliability. Compared with conventional automation, the proposed framework offers greater adaptability, higher sorting accuracy, and improved operational performance in dynamic production environments. The study concludes that intelligent robotic pick-and-sort systems are a key technology for smart factories, supporting flexible manufacturing, mass customization, and sustainable industrial production, with future opportunities in digital twins, explainable AI, cloud-edge intelligence, and collaborative human-robot systems.

  • References

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

    [2] J. Redmon and A. Farhadi, “YOLOv3: An Incremental Improvement,” arXiv preprint arXiv:1804.02767, 2018.

    [3] 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.

    [4] K. He, G. Gkioxari, P. Dollár, and R. Girshick, “Mask R-CNN,” Proceedings of the IEEE International Conference on Computer Vision (ICCV), pp. 2961–2969, 2017.

    [5] M. Tan, R. Pang, and Q. V. Le, “EfficientDet: Scalable and Efficient Object Detection,” Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 10781–10790, 2020.

    [6] R. S. Sutton and A. G. Barto, Reinforcement Learning: An Introduction, 2nd ed. Cambridge, MA, USA: MIT Press, 2018.

    [7] S. Russell and P. Norvig, Artificial Intelligence: A Modern Approach, 4th ed. Pearson Education, 2021.

    [8] B. Siciliano and O. Khatib, Springer Handbook of Robotics, 2nd ed. Cham, Switzerland: Springer, 2016.

    [9] J. J. Craig, Introduction to Robotics: Mechanics and Control, 4th ed. Pearson, 2018.

    [10] R. Szeliski, Computer Vision: Algorithms and Applications, 2nd ed. Springer, 2022.

    [11] A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet Classification with Deep Convolutional Neural Networks,” Communications of the ACM, vol. 60, no. 6, pp. 84–90, 2017.

    [12] L. Deng and D. Yu, “Deep Learning: Methods and Applications,” Foundations and Trends in Signal Processing, vol. 7, no. 3–4, pp. 197–387, 2014.

    [13] K. P. Murphy, Probabilistic Machine Learning: An Introduction. Cambridge, MA, USA: MIT Press, 2022.

    [14] M. Grieves and J. Vickers, “Digital Twin: Mitigating Unpredictable, Undesirable Emergent Behavior in Complex Systems,” in Transdisciplinary Perspectives on Complex Systems, Springer, 2017, pp. 85–113.

    [15] S. Yin, X. Li, H. Gao, and O. Kaynak, “Data-Based Techniques Focused on Modern Industry: An Overview,” IEEE Transactions on Industrial Electronics, vol. 62, no. 1, pp. 657–667, 2015.

  • Downloads

Similar Articles

51-60 of 84

You may also start an advanced similarity search for this article.