Inverse robot calibration using artificial neural networks

Engineering Applications of Artificial Intelligence

Volumn 9, Issue 1, 1996, Pages 83-93

  Zhong, Xiaolin ^a   Lewis, John M ^b   N Nagy, Francis L ^b  

^a EDINBURGH NAPIER UNIVERSITY   (United Kingdom)

^b NONE

Author keywords

Artificial neural networks; Autonomous calibration; Generic accuracy model; Higher order networks; Inverse compensation; Robot calibration

Indexed keywords

CALIBRATION; COMPUTER NETWORKS; COMPUTER SIMULATION; END EFFECTORS; INVERSE KINEMATICS; MATHEMATICAL MODELS; MATHEMATICAL TRANSFORMATIONS; POSITION MEASUREMENT; ROBOT PROGRAMMING; ROBOTICS;

AUTONOMOUS CALIBRATION; GENERIC ACCURACY MODEL; HIGHER ORDER NETWORKS; INVERSE COMPENSATION; ROBOT CALIBRATION; ROBOT CONTROLLER;

NEURAL NETWORKS;

EID: 0030086616     PISSN: 09521976     EISSN: None     Source Type: Journal    
DOI: 10.1016/0952-1976(95)00069-0     Document Type: Article

References (20)

1. Hayati S. A. and Roston G. P. Inverse kinematic solution for near-simple robots and its application to robot calibration. In Recent Trends in Robotics: Modelling, Control, and Education, pp. 41-49. Elsevier, Amsterdam (1986).
2. Shamma J. S. and Whitney D. E. A method for inverse robot calibration. J. Dynamic Syst. Measmt. Cont. 109, 36-43 (1987).
3. Roth Z. S., Mooring B. W. and Ravani B. An overview of robot calibration. IEEE J. Robotics Automn Ra-3, 337-385 (1987).
4. Mooring B. W., Roth Z. S. and Driels, M. R. Fundamentals of Manipulator Calibration, pp. 221-225. Wiley, New York (1991).
5. Stone H. W., Sanderson A. C. and Neuman C. P. Arm Signature Identification. Proc. IEEE Int. Conf. on Robotics and Automation, pp. 41-48. San Francisco, CA (1986).
6. Zhuang H. Kinematic modeling, Identification, and compensation of robot manipulators. Ph.D. thesis, Florida Atlantic University (1989).
7. Kirchner H. O., Gurumoorthy B. and Printz F. B. A perturbation approach to robot calibration. Int. J. Robotics Res. 6, 47-59 (1987).
8. Mirman C. R. and Gupta K. C. Compensation of robot joint variables using special Jacobian matrices. J. Robotic Syst. 9, 113-137 (1992).
9. Zhuang H., Hamano F. and Roth Z. S. Optimal design of robot accuracy compensators. Proc. IEEE Int. Conf. Robotics and Automation, Arizona, pp. 751-756 (1989).
10. Kozakiewicz C., Ogiso T. and Miyake P. Calibration analysis of a direct drive robot. IEEE Int. Workshop on Intelligent Robot and Systems, Japan, pp. 213-220 (1990).
11. Guez A. and Ahmad Z. Solution to the inverse kinematics problem in robotics by neural networks. Proc. IEEE Int. Conf. on Neural Networks, U.S.A., pp. 617-624 (1988).
12. Zhong X. L., Lewis J. M. and Rea H. Neuro-accuracy compensator for industrial robots. Proc. IEEE Int. Conf. on Neural Networks, Orlando, Florida, Vol. 5, pp. 2797-2802 (1994).
13. Hopfield J. J. and Tank D. W. Neural computation of decision in optimisation problems. Biol. Cybernetics 52, 141-152 (1985).
14. Wu C. H., Ho J. and Young K. Design of robot accuracy compensator after calibration. IEEE Int. Conf. on Robotics and Automation, U.S.A., pp. 780-785 (1988).
15. N-Nagy F. L. and Siegler T. Engineering Foundation of Robotics, pp. 26-28. Prentice-Hall, Englewood Cliffs, NJ (1987).
16. Ghosh J. and Shin Y. Efficient higher-order neural networks for classification and function approximation, Int. J. Neural Syst. 3, pp. 323-350.
17. Stone H. W. Kinematic Modelling, Identification and Control of Robotic Manipulators. Chap. 5, pp. 78-95. Kluwer Academic Publishers (1992).
18. Zhong X. L. and Lewis J. M. Kinematic identification and compensation of robot manipulators using neural optimisation networks. Proc. 3rd Int. Conf. on Automation, Robotics and Computer Vision, Singapore, Vol. III, pp. 1472-1476 (1994).
19. Everett L. J., Colson J. C. and Mooring B. W. Automatic singularity avoidance using joint variations in robot task modification. IEEE Robotics & Automation Magazine 1, 13-19 (1994).
20. Cichocki A. and Unbehauen R. Neural Networks for Optimization and Signal Processing. Wiley, New York (1993).