Autonomous Robotic Calibration Techniques for High-Precision Manufacturing
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DOI:
https://doi.org/10.67228/30715725/IJIARE-2021PI7H5WPublished 01-03-2021
Autonomous Robotics, Robotic Calibration, High-Precision Manufacturing, Intelligent Manufacturing, Artificial Intelligence, Machine Vision, Sensor Fusion, Industrial Internet of Things, Smart Manufacturing, Predictive Maintenance Issue
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ArticlesHow to Cite
[1]S. N, “Autonomous Robotic Calibration Techniques for High-Precision Manufacturing”, IJIARE, vol. 4, no. 1, pp. 01–16, Jan. 2021, doi: 10.67228/30715725/IJIARE-2021PI7H5W.Abstract
Industry 4.0 technologies, including AI, IIoT, robotics, and cyber-physical systems, require industrial robots to maintain high positioning accuracy despite thermal, mechanical, and operational changes. Conventional offline calibration is time-consuming, costly, and unsuitable for dynamic manufacturing environments. The proposed autonomous calibration framework integrates multi-sensor fusion, machine vision, laser measurement, inertial sensing, machine learning, and adaptive optimization to continuously estimate and compensate for calibration errors in real time. By updating robot kinematic models during operation, the system improves positioning accuracy, repeatability, manufacturing quality, equipment utilization, and predictive maintenance while minimizing downtime and human intervention. Overall, the framework enables self-learning, real-time robotic calibration that supports intelligent, efficient, and sustainable smart manufacturing.
References
[1] J. M. Hollerbach and C. W. Wampler, "The Calibration Index and Taxonomy for Robot Kinematic Calibration Methods," The International Journal of Robotics Research, vol. 15, no. 6, pp. 573–591, Dec. 1996.
[2] W. Khalil and E. Dombre, Modeling, Identification and Control of Robots. Oxford, U.K.: Butterworth-Heinemann, 2002.
[3] B. Mooring, Z. Roth, and M. Driels, Fundamentals of Manipulator Calibration. New York, NY, USA: Wiley, 1991.
[4] A. Nubiola and I. A. Bonev, "Absolute Calibration of an ABB IRB 1600 Robot Using a Laser Tracker," Robotics and Computer-Integrated Manufacturing, vol. 29, no. 1, pp. 236–245, Feb. 2013.
[5] R. Tsai, "A Versatile Camera Calibration Technique for High-Accuracy 3D Machine Vision Metrology," IEEE Journal of Robotics and Automation, vol. 3, no. 4, pp. 323–344, Aug. 1987.
[6] Z. Zhang, "A Flexible New Technique for Camera Calibration," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 22, no. 11, pp. 1330–1334, Nov. 2000.
[7] R. I. Hartley and A. Zisserman, Multiple View Geometry in Computer Vision, 2nd ed. Cambridge, U.K.: Cambridge University Press, 2004.
[8] G. Welch and G. Bishop, "An Introduction to the Kalman Filter," University of North Carolina, Chapel Hill, NC, USA, Tech. Rep. TR95-041, 2006.
[9] S. Thrun, W. Burgard, and D. Fox, Probabilistic Robotics. Cambridge, MA, USA: MIT Press, 2005.
[10] Y. LeCun, Y. Bengio, and G. Hinton, "Deep Learning," Nature, vol. 521, no. 7553, pp. 436–444, May 2015.
[11] I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning. Cambridge, MA, USA: MIT Press, 2016.
[12] R. S. Sutton and A. G. Barto, Reinforcement Learning: An Introduction, 2nd ed. Cambridge, MA, USA: MIT Press, 2018.
[13] M. Grieves and J. Vickers, "Digital Twin: Mitigating Unpredictable, Undesirable Emergent Behavior in Complex Systems," in Transdisciplinary Perspectives on Complex Systems. Cham, Switzerland: Springer, 2017, pp. 85–113.
[14] L. Monostori, "Cyber-Physical Production Systems: Roots, Expectations and R&D Challenges," Procedia CIRP, vol. 17, pp. 9–13, 2014.
[15] S. Wang, J. Wan, D. Li, and C. Zhang, "Implementing Smart Factory of Industrie 4.0: An Outlook," International Journal of Distributed Sensor Networks, vol. 12, no. 1, pp. 1–10, 2016.
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How to Cite
[1]S. N, “Autonomous Robotic Calibration Techniques for High-Precision Manufacturing”, IJIARE, vol. 4, no. 1, pp. 01–16, Jan. 2021, doi: 10.67228/30715725/IJIARE-2021PI7H5W.
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