機器人校正與醫學影像導引定位應用

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姓名

林芃威(Pon-Wei Lin) 查詢紙本館藏

畢業系所

機械工程學系

論文名稱

機器人校正與醫學影像導引定位應用
(Robot calibration and its application in medical image-guided positioning)

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摘要(中)

由於機器人的穩定性高及可遠端遙控，所以機器人在特殊手術上如微創手術可以協助醫生完成人手難以達成的手術。一般而言，機器人的定位精度不能達到手術的精度要求，必須校正其定位精度。本研究提出正交神經網路補償方法以校正機器人的定位精度，並將機器人應用於影像導引系統中，以驗證其校正結果。首先，以光學定位裝置自動地量測機器人在特定位置(關節角)的方位誤差，並計算出對應的補償量，再利用正交神經網路訓練出機器人的關節角和方位補償量之間的函數關係，並以正交神經網路的輸出補償機器人的定位誤差。本研究以CRS F3機器人進行校正實驗，在校正前機器人的最大位置誤差(誤差機率分佈中2個標準差內的誤差)為20.8mm，經校正後，其最大位置誤差縮小至1.7mm，方向最大誤差也從校正前的3.9度改善為校正後的0.71度，大幅地提升機器人的定位精度。在影像導引機器人定位上，以顱骨模型進行導引實驗，由實驗結果顯示平均位置誤差為2.14mm，平均方向誤差為0.91度。

摘要(英)

Due to the reliable stability and tele-manipulability, robot is suitable to assist surgeons to do specified operations such as Minimally Invasive Surgery. Usually, the positioning error of robot is bigger than the required positioning accuracy of operation. Therefore, robot calibration is necessary to improve the positioning accuracy. This study presents an ONN compensation method for the calibration of a six-axis robot, and its performance is verified by applying to image-guided robotic positioning. First, an optical localizer is applied to measure the positioning errors and to determine corresponding position/orientation compensations of the robot at predefined positions (joint angles). The two sets of the joint angles and their position/orientation compensations are brought to Orthogonal Neural Network to establish the mapping relations. Then, the output position/orientation compensations for a given arbitrary set of joint angles are used to compensate positioning error. The CRS F3 robot is used in calibration experiment. The calibration result shows that the average positioning accuracy has been improved from initial 20.8mm to compensated 1.7mm in distance and from initial 3.9 degree to compensated 0.71 degree in orientation. Further, the positioning accuracy of the calibrated robot has been verified in image-guided robotic positioning. The experimental result shows that the average position error is 2.14 mm and orientation error is 0.91 degree.

關鍵字(中)

★ 正交神經網路
★ 機器人校正
★ 影像導引手術

關鍵字(英)

★ Robot calibration
★ Orthogonal neural network
★ Image-guided surgery

論文目次

摘要 I
目錄 II
圖索引 IV
表索引 VII
第一章緒論 1
第二章系統架構 7
第三章正交神經網路與機器人校正 12
第四章影像導引機器人手術 40
第五章實驗結果與討論 46
第六章結論 73
參考文獻 75

參考文獻

[1] Abche, A. B., Tzanakos, G. S., and Tzanakou, E. M., “A method for multimodal 3-D image registration with external markers”, Proc. IEEE Int. Conf. on Engineering in Medicine and Biology Society, Vol. 5, pp 1881-1882. Oct/Nov 1992.
[2] Abche, A. B., Tzanakos, G. S., and Tzanakou, E. M., “Effect of the number of external markers in multimodal 3-D image registration”, Proc. IEEE Nineteenth Annual Northeast Bioengineering Conf., pp. 172-173, Mar 1993.
[3] Bennett, D. J. and Hollerbach, J. M., “Identifying the kinematics of robots and their tasks”, Proc. IEEE Int. Conf. on Robotics and Automation, Vol. 1, pp. 580-586,May. 1989.
[4] Besl, P. J. and McKay, N. D., “A method for registration of 3-D shapes”, IEEE Trans. On Pattern Analysis and Machine Intelligence, Vol. 14, No. 2, pp. 239-256, Feb. 1992.
[5] Chen, J. and Chao, L. M., “Positioning error analysis for robot manipulators with all rotary joints”, IEEE J. of Robotics and Automation, Vol. 3, No. 6, pp. 539-545, Dec. 1987.
[6] Dario, P. and Menciassi, A., “Robotics for surgery”, 2002. 24th Annual Conference and the Annual Fall Meeting of the Biomedical Engineering Society, pp 1813-1814, 2002.
[7] Judd, R. P. and Knasinski, A. B., “A technique to calibrate industrial with experimental verification”, IEEE Trans. on Robotics and Automation, Vol. 6, No. 1, pp. 20-30, Feb. 1990.
[8] Kyle, S., “Optical methods for calibrating and inspecting robots”, Computing and Control Engineering J., Vol. 6, No. 4, pp. 166-173, Aug. 1995.
[9] Kyle, S., Meyer, R., and Albada, G. D., “Robot calibrating by optical methods”, IEEE Colloquium on Next Steps for Industrial Robotics, pp. 1-6, May 1994.
[10] Lorensen, W. E. and Cline, H. E., “Marching cubes: a high resolution 3D surface construction algorithm”, Computer Graphics, Vol. 21, No. 4, July 1987.
[11] Maurer, C. R., Aboutanos, G. B., Dawant, B. M., et al., “Registration of 3-D images using weighted geometrical features”, IEEE Trans on Medical Imaging, Vol. 15, No. 6, pp. 836-849, Dec. 1996.
[12] Maurer, C. R., Maciunas, R. J., and Fitzpatrick, J. M., “Registration of head CT images to physical space using a weighted combination of points and surfaces”, IEEE Trans on Medical Imaging, Vol. 17, No. 5, pp. 753-761, Oct. 1998.
[13] Mooring, B. W., and Pack, T. J., “Calibration procedure for an industrial robot”, Proc. IEEE Int. Conf. on Robotics and Automation, Vol. 2, pp. 786-791, 1988.
[14] Mooring, B. W. and Padavala, S. S., “The effect of kinematic model complexity on manipulator accuracy”, Proc. IEEE Int. Conf. on Robotics and Automation, Vol. 1, pp. 593-598, 1989.
[15] Nakamura, H., Itaya, T., Yamamoto, k., et al., “Robot autonomous error calibration method for off line programming system”, Proc. IEEE Int. Conf. on Robotics and Automation, Vol. 2, pp. 1775-1782, May 1995.
[16] Newman, W. S. and Osborn, D. W., “A new method for kinematic parameter calibration via laser line tracking”, Proc. IEEE Int. Conf. on Robotics and Automation, Vol.2, pp 160-165, May 1993.
[17] Qian, L. and Mavroidis, C., “Identification of the end-effector positioning errors of a high accuracy large medical robot using neural network”, Proc. IEEE Int. Conf. on Intelligent Robots and Systems, Vol. 2, pp. 951-958, Oct. 1998.
[18] Roth, Z. S., Mooring, B. W., and Ravani, B., “An overview of robot calibration”, IEEE J. of Robotics and Automation, Vol. 3, No 5, pp. 377-385, Oct. 1987.
[19] Schilling, R. J., “Fundamentals of robotics analysis and control”, Prentice-Hall, 1998.
[20] Schroeder, W. J., “The vtk user’s guide”, Kitware, Inc., 2002.
[21] Schroeder, W., Martin, K., and Lorensen, B., “The visualization toolkit”, 2nd Edition, Prentice-Hall, New Jersey, 1998.
[22] Shamma, J. S. and Whitney, D. E., “A method for inverse robot calibration”, J. of Dynamic Systems, Measurement, and Control, Vol. 109, pp. 36-43.Mar. 1987.
[23] Tseng, C. S. and Yang, S. S., “A new orthogonal neural network”, Proc. IEEE Int. Conf. on Neural Networks, Vol. 1, pp 296-299, Nov./Dec. 1995.
[24] Veitshegger, W., and Wu, C. H., “A method for calibrating and compensating robot kinematic errors”, Proc. IEEE Int. Conf. on Robotics and Automation, Vol.4, pp. 39-44, Mar 1987.
[25] Wu, G. and Wang, J., “A recurrent neural network for manipulator inverse kinematics computation”, IEEE Int. Conf. on Neural Networks, Vol. 5, pp. 2715-2720, Jun./Jul.1994.
[26] Xu, W. and Mills, J. K., “A new approach to the position and orientation calibration of robots”, Proc. IEEE Int. Sym. On Assemble and Task Planning Porto, pp. 268-273, July 1999.
[27] Yang, S. S. and Tseng, C. S., “An orthogonal neural network for function approximation”, IEEE Trans. on Systems, Man, and Cybernetics, Part B, Vol. 26, No. 5, pp. 779-785, Oct 1996.
[28] Zhang, H., Motaghedi, S. H., and Roth, Z. S., “Robot calibration with planar constraints”, Proc. IEEE Int. Conf. on Robotics and Automation, Vol. 1, pp 805-810, 1999.
[29] Zhang, H. and Paul, R. P., “Non-kinematic errors in robot manipulators”, Proc. IEEE Int. Conf. on Robotics and Automation, Vol. 2, pp. 1138-1139, Apr. 1988.
[30] Zhong, X. L. and Lewis, J. M., “A new method for autonomous robot calibration”, Proc. IEEE Int. Conf. on Robotics and Automation, Vol. 2, pp 1790-1795, May 1995.
[31] Zhong, X. L., Lewis, J. M., and Rea, H., “Neuro-accuracy compensator for industrial robots”, IEEE Int. Conf. on Neural Networks, Vol. 5, pp. 2797-2802, Jun./Jul. 1994.
[32] 余介凡, “正交神經網路之建構”, 碩士碩文，中央大學機械所, 1995.
[33] 黃教琪, “手術用影像導引機械人定位及鑽孔系統”, 碩士碩文, 中央大學機械所, Jun. 2001.
[34] 莊克士, “醫學影像物理學”, 合記圖書出版社, 1998.
[35] 葉怡成, “應用類神經網路”，儒林圖書公司，1997.
[36] 楊秀雄, “以正交函數為基的適應性網路及其應用”, 碩士碩文, 中央大學機械所, Jun. 1993.
[37] 蘇木春, 張孝德, “機器學習: 類神經網路、模糊系統以及基因演算法則”, 全華科技圖書, 1999.
[38] CRS robot, http://www.robotsdotcom.com/
[39] Sugal 2.1 genetic algorithms simulator, http://www.dur.ac.uk/andrew1.hunter/Sugal/
[40] The visualization toolkit, http://public.kitware.com/VTK/

指導教授

曾清秀(Ching-Show Tseng)

審核日期

2003-7-11

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