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Binocular vision and priori data based intelligent pose measurement method of large aerospace cylindrical components

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Abstract

In the robot finishing process of the assembly interface of large aerospace cylindrical components (short for assembly interface), to realize the high-precision and high-efficiency pose perception of the large component, an intelligent pose measurement method for the large component is proposed based on binocular vision and priori data. In this method, the global coordinate system of the robot finishing system is initially established using laser tracking measurement method and customized reference plates, giving a unified coordinate transformation datum for the interoperation of the finishing system's subsystems. Then, utilizing deep learning and digital image processing technologies, an algorithm for recognizing and locating key features of the large component is developed, which can realize the identification of key feature types and accurate localization of feature centroids. Following that, the global coordinate of the key feature centroid is determined using the proposed binocular vision three-dimensional (3D) coordinate reconstruction method. Meanwhile, by introducing the priori processing data of the large component to match the 3D reconstruction coordinates of the key feature centroids, the spatial pose of the large component can be calculated with high precision. Finally, the proposed method is experimentally validated using a case study of a large aerospace cylindrical component. Experimental results prove that the proposed method can achieve high-precision pose measurement of the large component, which can provide pose data support for the adjustment or modification of the cutting path of the robot that is generated by the as-designed model of the large component, to ensure the correctness of the robotic machining of the assembly interface, and thus the proposed method can meet the robot finishing needs of the large component.

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References

  • Alhayani, B. S. (2021). Visual sensor intelligent module-based image transmission in industrial manufacturing for monitoring and manipulation problems. Journal of Intelligent Manufacturing, 32(2), 597–610.

    Article  Google Scholar 

  • Beschi, M., Mutti, S., Nicola, G., Faroni, M., Magnoni, P., Villagrossi, E., & Pedrocchi, N. (2019). Optimal robot motion planning of redundant robots in machining and additive manufacturing applications. Electronics, 8(12), 1437.

    Article  Google Scholar 

  • Chai, J., Zeng, H., Li, A., & Ngai, E. W. (2021). Deep learning in computer vision: A critical review of emerging techniques and application scenarios. Machine Learning with Applications, 6, 100134.

    Article  Google Scholar 

  • Fan, B., Ji, Q., Li, M., Xie, B., Feng, P., & Wang, B. (2019). iGPS and laser tracker applications comparison in digital assembly of large aircraft parts. Aeronautical Manufacturing Technology, 62(5), 57–62.

    Google Scholar 

  • Fan, W., Zheng, L., Ji, W., Xu, X., Lu, Y., & Wang, L. (2021). A machining accuracy informed adaptive positioning method for finish machining of assembly interfaces of large-scale aircraft components. Robotics and Computer-Integrated Manufacturing, 67, 102021.

    Article  Google Scholar 

  • Fan, W., Zheng, L., Ji, W., Xu, X., Wang, L., Lu, Y., & Zhao, X. (2020). Function block-based closed-loop adaptive machining for assembly interfaces of large-scale aircraft components. Robotics and Computer-Integrated Manufacturing, 66, 101994.

    Article  Google Scholar 

  • Fang, W., Fan, W., Ji, W., Han, L., Xu, S., Zheng, L., & Wang, L. (2022). Distributed cognition-based localization for AR-aided collaborative assembly in industrial environments. Robotics and Computer-Integrated Manufacturing, 75, 102292.

    Article  Google Scholar 

  • Feng, X., Jiang, Y., Yang, X., Du, M., & Li, X. (2019). Computer vision algorithms and hardware implementations: A survey. Integration, 69, 309–320.

    Article  Google Scholar 

  • Ji, W., & Wang, L. (2019). Industrial robotic machining: A review. The International Journal of Advanced Manufacturing Technology, 103(1), 1239–1255.

    Article  Google Scholar 

  • Jing, X., Zhang, P., Wang, Z., & Zhao, G. (2015). Digital combined measuring technology assisted quality inspection for aircraft assembly. Journal of Beijing University of Aeronautics and Astronautics, 41(7), 1196–1201.

    Google Scholar 

  • Li, J., Zhou, Q., Huang, X., Li, M., & Cao, L. (2021). In situ quality inspection with layer-wise visual images based on deep transfer learning during selective laser melting. Journal of Intelligent Manufacturing, 2021, 1–15.

    Google Scholar 

  • Liao, W., Zheng, K., Sun, L., Dong, S., & Zhang, L. (2022). Review on chatter stability in robotic machining for large complex components. Acta Aeronauticaet Astronautica Sinica, 43(1), 026061.

    Google Scholar 

  • Muelaner, J. E., Wang, Z., Martin, O., Jamshidi, J., & Maropoulos, P. G. (2012). Verification of the indoor GPS system, by comparison with calibrated coordinates and by angular reference. Journal of Intelligent Manufacturing, 23(6), 2323–2331.

    Article  Google Scholar 

  • Sahu, R. K. (2021). A review on application of laser tracker in precision positioning metrology of particle accelerators. Precision Engineering, 71, 232–249.

    Article  Google Scholar 

  • Sładek, J., Błaszczyk, P. M., Kupiec, M., & Sitnik, R. (2011). The hybrid contact–optical coordinate measuring system. Measurement, 44(3), 503–510.

    Article  Google Scholar 

  • Sui, S., & Zhu, X. (2020). Digital measurement technique for evaluating aircraft final assembly quality. Scientia Sinica Technologica, 50(11), 1449–1460.

    Article  Google Scholar 

  • Tao, B., Zhao, X., Li, R., & Ding, H. (2020). Research on robotic measurement-operation-machining technology and its application. China Mechanical Engineering, 31(01), 49–56.

    Google Scholar 

  • Tian, W., Jiao, J., Li, B., & Jiao, G. (2020). High precision robot operation equipment and technology in aerospace manufacturing. Journal of Nanjing University of Aeronautics & Astronautics, 52(3), 341–352.

    Google Scholar 

  • Verl, A., Valente, A., Melkote, S., Brecher, C., Ozturk, E., & Tunc, L. (2019). Robots in machining. CIRP Annals, 68(2), 799–822.

    Article  Google Scholar 

  • Wang, J., Gong, Z., Tao, B., & Yin, Z. (2022). A 3-D reconstruction method for large freeform surfaces based on mobile robotic measurement and global optimization. IEEE Transactions on Instrumentation and Measurement, 71, 1–9.

    Article  Google Scholar 

  • Wang, L., Muralikrishnan, B., Hernandez, O. I., Shakarji, C., & Sawyer, D. (2020). Performance evaluation of laser trackers using the network method. Measurement, 165, 108165.

    Article  Google Scholar 

  • Wen, K., Zhang, J., Yue, Y., Zhou, Y., Yang, J., & Chen, Q. (2021). Method for improving accuracy of NC-driven mobile milling robot. Journal of Mechanical Engineering, 57(05), 72–80.

    Article  Google Scholar 

  • Wu, C., Yang, L., Luo, Z., & Jiang, W. (2022). Linear laser scanning measurement method tracking by a binocular vision. Sensors, 22(9), 3572.

    Article  Google Scholar 

  • Xie, F., Mei, B., Liu, X., Zhang, J., & Yue, Y. (2020). Novel mode and equipment for machining large complex components. Journal of Mechanical Engineering, 56(19), 70–78.

    Article  Google Scholar 

  • Xu, A., Jia, Y., & Zhao, C. (2017). Research on precision forming technology for large integral panel with flanges of a spacecraft. Manned Spaceflight, 23(5), 619–625.

    Google Scholar 

  • Xu, S., Wang, J., Shou, W., Ngo, T., Sadick, A. M., & Wang, X. (2021). Computer vision techniques in construction: A critical review. Archives of Computational Methods in Engineering, 28(5), 3383–3397.

    Article  Google Scholar 

  • Ye, J., Niu, Z., Zhang, X., Wang, W., & Xu, M. (2020). In-situ deflectometic measurement of transparent optics in precision robotic polishing. Precision Engineering, 64, 63–69.

    Article  Google Scholar 

  • Yuan, P., Chen, D., Wang, T., Liu, Y., Cao, S., & Cai, Y. (2018). Research on positional error compensation method based on binocular vision measurement system. Aeronautical Manufacturing Technology, 61(4), 41–46.

    Google Scholar 

  • Zhang, Z. (2000). A flexible new technique for camera calibration. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(11), 1330–1334.

    Article  Google Scholar 

  • Zhang, Z., Xu, K., Wu, Y., Zhang, S., & Qi, Y. (2022). A simple and precise calibration method for binocular vision. Measurement Science and Technology, 33(6), 065016.

    Article  Google Scholar 

  • Zhou, S., Guo, Y., Gao, C., & Wu, X. (2014). Rapid length measuring system for mobile and large-scale cylinder workpiece based on 3D laser scanning. Optics and Precision Engineering, 22(06), 1524–1530.

    Article  Google Scholar 

  • Zhou, Y., Li, Q., Chu, L., Ma, Y., & Zhang, J. (2020). A measurement system based on internal cooperation of cameras in binocular vision. Measurement Science and Technology, 31(6), 065002.

    Article  Google Scholar 

  • Zhou, Z., Liu, W., Wang, Y., Yu, B., Cheng, X., Yue, Y., & Zhang, J. (2022). A combined calibration method of a mobile robotic measurement system for large-sized components. Measurement, 189, 110543.

    Article  Google Scholar 

  • Zhu, D., Feng, X., Xu, X., Yang, Z., Li, W., Yan, S., & Ding, H. (2020b). Robotic grinding of complex components: A step towards efficient and intelligent machining–challenges, solutions, and applications. Robotics and Computer-Integrated Manufacturing, 65, 101908.

    Article  Google Scholar 

  • Zhu, X., Liu, L., & Chen, X. (2020a). Measurement station optimization for laser tracker in-situ using based on Monte-Carlo simulation. Computer Integrated Manufacturing Systems, 26(11), 3001–3010.

    Google Scholar 

  • Zhu, Z., Tang, X., Chen, C., Peng, F., Yan, R., Zhou, L., & Wu, J. (2021). High precision and efficiency robotic milling of complex parts: Challenges, approaches and trends. Chinese Journal of Aeronautics, 35(2), 22–46.

    Article  Google Scholar 

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Acknowledgements

This research received support from the National Natural Science Foundation of China under Grant No. 52205511 and Defense Industrial Technology Development Program of China under Grant No. JCKY2021204B045. The authors gratefully acknowledge to the members of the digital and intelligent manufacturing research group at Beihang University, who provide satisfactory work for this research.

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Authors and Affiliations

  1. School of Mechanical Engineering and Automation, Beihang University, Beijing, 100191, China

    Wei Fan, Qiang Fu, Yansheng Cao, Lianyu Zheng & Xuexin Zhang

  2. MIIT Key Laboratory of Intelligent Manufacturing Technology for Aeronautics Advanced Equipment, Ministry of Industry and Information Technology, Beijing, 100191, China

    Wei Fan & Lianyu Zheng

  3. Beijing Institute of Electronic System Engineering, Beijing, 100854, China

    Qiang Fu

  4. Beijing Xinfeng Aerospace Equipment Co., Ltd, Beijing, 100854, China

    Yansheng Cao & Jieru Zhang

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  1. Wei Fan
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  2. Qiang Fu
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  3. Yansheng Cao
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  4. Lianyu Zheng
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Correspondence to Lianyu Zheng.

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Fan, W., Fu, Q., Cao, Y. et al. Binocular vision and priori data based intelligent pose measurement method of large aerospace cylindrical components. J Intell Manuf 35, 2137–2159 (2024). https://doi.org/10.1007/s10845-023-02143-y

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