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CLC number: TP391.4

On-line Access: 2014-08-06

Received: 2013-12-29

Revision Accepted: 2014-03-03

Crosschecked: 2014-07-16

Cited: 2

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Citations:  Bibtex RefMan EndNote GB/T7714

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Journal of Zhejiang University SCIENCE C 2014 Vol.15 No.8 P.593-606

http://doi.org/10.1631/jzus.C1300379


Development of a monocular vision system for robotic drilling


Author(s):  Wei-dong Zhu, Biao Mei, Guo-rui Yan, Ying-lin Ke

Affiliation(s):  Department of Mechanical Engineering, Zhejiang University, Hangzhou 310027, China; more

Corresponding email(s):   wdzhu@zju.edu.cn

Key Words:  Vision system, Robotic drilling, Error measurement, Elliptical contour extraction, Hand-eye calibration


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Wei-dong Zhu, Biao Mei, Guo-rui Yan, Ying-lin Ke. Development of a monocular vision system for robotic drilling[J]. Journal of Zhejiang University Science C, 2014, 15(8): 593-606.

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Abstract: 
robotic drilling for aerospace structures demands a high positioning accuracy of the robot, which is usually achieved through error measurement and compensation. In this paper, we report the development of a practical monocular vision system for measurement of the relative error between the drill tool center point (TCP) and the reference hole. First, the principle of relative error measurement with the vision system is explained, followed by a detailed discussion on the hardware components, software components, and system integration. The elliptical contour extraction algorithm is presented for accurate and robust reference hole detection. System calibration is of key importance to the measurement accuracy of a vision system. A new method is proposed for the simultaneous calibration of camera internal parameters and hand-eye relationship with a dedicated calibration board. Extensive measurement experiments have been performed on a robotic drilling system. Experimental results show that the measurement accuracy of the developed vision system is higher than 0.15 mm, which meets the requirement of robotic drilling for aircraft structures.

用于机器人制孔的单目视觉系统开发

研究目的:在航空结构件机器人制孔中,通常通过误差测量和补偿保证制孔位置精度。本文旨在开发一种低成本、高精度的视觉系统,通过集成鲁棒的椭圆特征提取算法、实用的视觉系统标定方法,实现对刀具和工件之间相对误差的精确测量。
创新要点:视觉测量相关研究中缺乏对视觉系统工作原理的深入阐释;视觉系统误差测量原理的精确阐述,为视觉系统的开发和测量精度的提高提供了理论基础。机器人制孔环境中存在大量噪声和环境干扰因素,视觉系统中集成的特征提取算法应具有较高的鲁棒性;基于显著性的椭圆轮廓提取算法可实现鲁棒、精确的基准孔检测。工业应用通常要求视觉系统的标定方法兼具实用性和精确性;本文方法提供了一种实用、精确、可同时实现相机和手眼关系标定的标定方法。
方法提亮:精确阐释了视觉系统的工作原理,为后续相机刀具中心点和视觉系统标定方法的确定提供了理论依据。通过集成显著性计算、投票方法和Snake模型,开发了一种鲁棒、精确的椭圆轮廓特征提取算法(图8)。提出了一种基于专用标定板的、可同时实现相机内参数和手眼关系标定的视觉系统标定方法,该方法可避免测量过程引入Abbe误差(图12)。
重要结论:本文研究了视觉测量系统的工作原理,并结合提出的椭圆轮廓提取算法、视觉系统标定方法,开发了一种低成本、满足机器人制孔精度要求的单目视觉测量系统。在机器人制孔系统平台上的实验证实,本文提出的视觉系统满足航空工业对制孔精度的要求,特征提取算法和标定方法鲁棒、有效。
视觉系统;机器人制孔;误差测量;椭圆轮廓提取;手眼关系标定

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Reference

[1]Armingol, J.M., Otamendi, J., de la Escalera, A., et al., 2003. Statistical pattern modeling in vision-based quality control systems. J. Intell. Robot. Syst., 37(3):321-336.

[2]Bilen, H., Hocaoglu, M., Unel, M., et al., 2012. Developing robust vision modules for microsystems applications. Mach. Vis. Appl., 23(1):25-42.

[3]Bone, G.M., Capson, D., 2003. Vision-guided fixtureless assembly of automotive components. Robot. Comput.-Integr. Manuf., 19(1-2):79-87.

[4]Bradski, G., Kaehler, A., 2008. Learning OpenCV: Computer Vision with the OpenCV Library. O’reilly, Sebastopol, CA.

[5]Chen, S., Zhang, Y., Qiu, T., et al., 2003. Robotic welding systems with vision-sensing and self-learning neuron control of arc welding dynamic process. J. Intell. Robot. Syst., 36(2):191-208.

[6]DeVlieg, R., Sitton, K., Feikert, E., et al., 2002. ONCE (ONe-sided Cell End effector) Robotic Drilling System. SAE Technical Paper 2002-01-2626.

[7]Dornaika, F., Horaud, R., 1998. Simultaneous robot-world and hand-eye calibration. IEEE Trans. Robot. Autom., 14(4):617-622.

[8]Eberli, D., Scaramuzza, D., Weiss, S., et al., 2010. Vision based position control for MAVs using one single circular landmark. J. Intell. Robot. Syst., 61(1-4):495-512.

[9]Fitzgibbon, A., Pilu, M., Fisher, R.B., 1999. Direct least square fitting of ellipses. IEEE Trans. Pattern Anal. Mach. Intell., 21(5):476-480.

[10]Forsyth, D.A., Ponce, J., 2011. Computer Vision: a Modern Approach (2nd Ed.). Prentice-Hall, Englewood Cliffs, New Jersey.

[11]Ibarguren, A., Martínez-Otzeta, J.M., Maurtua, I., 2014. Particle filtering for industrial 6DOF visual servoing. J. Intell. Robot. Syst., 74(3-4):689-696.

[12]Lee, B., Tarng, Y., 2001. Surface roughness inspection by computer vision in turning operations. Int. J. Mach. Tool. Manuf., 41(9):1251-1263.

[13]Malassiotis, S., Strintzis, M.G., 2003. Stereo vision system for precision dimensional inspection of 3D holes. Mach. Vis. Appl., 15(2):101-113.

[14]Malti, A., 2012. Hand–eye calibration with epipolar constraints: application to endoscopy. Robot. Auton. Syst., 61(2):161-169.

[15]Motta, J.M.C.S., de Carvalho, G.C., McMaster, R., 2001. Robot calibration using a 3D vision-based measurement system with a single camera. Robot. Comput.-Integr. Manuf., 17(6):487-497.

[16]Neto, H.V., Nehmzow, U., 2007. Real-time automated visual inspection using mobile robots. J. Intell. Robot. Syst., 49(3):293-307.

[17]Olsson, T., Haage, M., Kihlman, H., et al., 2010. Cost-efficient drilling using industrial robots with high-bandwidth force feedback. Robot. Comput.-Integr. Manuf., 26(1):24-38.

[18]Otsu, N., 1979. A threshold selection method from gray-level histograms. IEEE Trans. Syst. Man Cybern., 9(1):62-66.

[19]Pachidis, T.P., Lygouras, J.N., 2007. Vision-based path generation method for a robot-based arc welding system. J. Intell. Robot. Syst., 48(3):307-331.

[20]Simonvsky, M., 2011. Ellipse Detection Using 1D Hough Transform. Available from http://www.mathworks.com/matlabcentral/fileexchange/33970-ellipse-detection-using-1d-hough-transform.

[21]Strobl, K.H., Hirzinger, G., 2008. More accurate camera and hand-eye calibrations with unknown grid pattern dimensions. IEEE Int. Conf. on Robotics and Automation, p.1398-1405.

[22]Strobl, K.H., Sepp, W., Hirzinger, G., 2009. On the issue of camera calibration with narrow angular field of view. IEEE/RSJ Int. Conf. on Intelligent Robots and Systems, p.309-315.

[23]Suzuki, S., 1985. Topological structural analysis of digitized binary images by border following. Comput. Vis. Graph. Image Process., 30(1):32-46.

[24]Thompson, P., Hartmann, J., Feikert, E., et al., 2005. Flex Track for Use in Production. SAE Technical Paper 2005-01-3318.

[25]Tsai, R., 1987. A versatile camera calibration technique for high-accuracy 3D machine vision metrology using off-the-shelf TV cameras and lenses. IEEE J. Robot. Autom., 3(4):323-344.

[26]Webb, P., Eastwood, S., Jayaweera, N., et al., 2005. Automated aerostructure assembly. Ind. Robot., 32(5):383-387.

[27]Williams, D.J., Shah, M., 1992. A fast algorithm for active contours and curvature estimation. CVGIP Image Understand., 55(1):14-26.

[28]Zhan, Q., Wang, X., 2012. Hand–eye calibration and positioning for a robot drilling system. Int. J. Adv. Manuf. Technol., 61(5-8):691-701.

[29]Zhang, Z., 2000. A flexible new technique for camera calibration. IEEE Trans. Pattern Anal. Mach. Intell., 22(11):1330-1334.

[30]Zhao, Z., Weng, Y., 2013. A flexible method combining camera calibration and hand–eye calibration. Robotica, 31(5):747-756.

[31]Zhou, Y., Nelson, B.J., Vikramaditya, B., 2000. Integrating optical force sensing with visual servoing for microassembly. J. Intell. Robot. Syst., 28(3):259-276.

[32]Zhu, W., Qu, W., Cao, L., et al., 2013. An off-line programming system for robotic drilling in aerospace manufacturing. Int. J. Adv. Manuf. Technol., 68(9-12):2535-2545.

[33]Zou, X., Zou, H., Lu, J., 2012. Virtual manipulator-based binocular stereo vision positioning system and errors modelling. Mach. Vis. Appl., 23(1):43-63.

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