Hostname: page-component-586b7cd67f-t7fkt Total loading time: 0 Render date: 2024-11-24T13:26:59.476Z Has data issue: false hasContentIssue false

Robot performance measurement and calibration using a 3D computer vision system

Published online by Cambridge University Press:  09 March 2009

B. Preising
Affiliation:
Robotics Research Laboratory, Department of Electrical and Computer Engineering, University of California, Davis, Davis, California 95616 (USA)
T. C. Hisa
Affiliation:
Robotics Research Laboratory, Department of Electrical and Computer Engineering, University of California, Davis, Davis, California 95616 (USA)

Summary

Present day robot systems are manufactured to perform within industry accepted tolerances. However, to use such systems for tasks requiring high precision, various methods of robot calibration are generally required. These procedures can improve the accuracy of a robot within a small volume of the robot's workspace. The objective of this paper is to demonstrate the use of a single camera 3D computer vision system as a position sensor in order to perform robot calibration. A vision feedback scheme, termed Vision-guided Robot Control (VRC), is described which can improve the accuracy of a robot in an on-line iterative manner. This system demonstrates the advantage that can be achieved by a Cartesian space robot control scheme when end effector position/orientation are actually sensed instead ofcalculated from the kinematic equations. The degree of accuracy is determined by setting a tolerance level for each of the six robot Cartesian space coordinates. In general, a small tolerance level requires a large number of iterations in order to position the end effector, and a large tolerance level requires fewer iterations. The viability of using a vision system for robot calibration is demonstrated by experimentally showing that the accuracy of a robot can be drastically improved. In addition, the vision system can also be used to determine the repeatability and accuracy of a robot in a simple, efficient, and quick manner. Experimental work with an IBM Electric Drive Robot (EDR) and the proposed vision system produced a 97 and a 145 fold improvement in the position and orientation accuracy of the robot, respectively.

Type
Articles
Copyright
Copyright © Cambridge University Press 1995

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

1.Riley, D. L., “Robot Calibration and Performance Specification Determination” Robots 11–17th Int. Symposium Industrial Robots Conf. Proc.,Chicago, Illinois.(April 26–30. 1987) pp. 10–1 to 510–17–23.Google Scholar
2.Lau, K. and Hocken, R., “A Survey of Current Robot Metrology MethodsAnnals of the CIRP 33, No. 2, 485488 (1984).Google Scholar
3.Ranky, P. G. and Wodzinski, M., “Accuracy” In: (Dorf, R. C., ed) International Encyclopedia of Robotics: Applications and Automation (John Wiley and Sons, New York, 1988) pp. 112.Google Scholar
4.McEntire, R. H., “Three Dimension Accuracy Measurement Methods for Robots” The Industrial Robot 105112 (09, 1976).Google Scholar
5.Ranky, P. G., “Test Method and Software for Robot Qualification” The Industrial Robot 111115 (06, 1984).Google Scholar
6.Warnecke, H. J. and Brodbeck, B., “Test Stand for Industrial Robots” Proc. 7th Int. Symp. Industrial Robots,Tokyo(Oct. 19–21. 1977) pp. 443451.Google Scholar
7.Brodbeck, B. and Schiele, G., “Measurements and Analyses of Geometrical Quantities Concerning Industrial Robots” Proc. 8th Int. Symp. Industrial Robots,Stuttgart(May 30–June 1, 1978) pp. 255268.Google Scholar
8.Mathews, S. H. and Hill, J. W., “Repeatability Test System for Industrial Robots” Society of Manufacturing Engineers paper No. MS84–1045 (1985).Google Scholar
9.Mooring, B. W. and Pack, T. J., “Determination and Specification of Robot Repeatability” Proceedings of 1986 IEEE Int. Conf. on Robotics and Automation,San Francisco, Calif.(April 1986) pp. 10171023.Google Scholar
10.Tucker, M., Perreira, N. D. and Nyman, D. H., “A Pose Measurement Sensor” Robots 11–17th Int. Symposium Industrial Robots Conf. Proc.,Chicago, Illinois(April 26–30, 1987) pp. 283 to 298.Google Scholar
11.Dainis, A. and Juberts, M., “Accurate Remote Measurement of Robot Trajectory Motion” Proc. IEEE Conf. Robotics Automation,St. Louis, Missouri(Mar. 25–28, 1985) pp. 9299.Google Scholar
12.Juberts, M., “Repeatability Measurements of a Vision Servoed Manipulator using an Optoelectronic Remote 3-D Tracking System” EEE 948955 (1985).Google Scholar
13.Myers, D. R., Juberts, M. and Leake, S. A., “Enhanced Telemanipulator Operation using a Passive Vision System” IEEE Proc. Int. Conf. Cybernetics and Society,Tuscson, Arizona,(Nov. 12–15, 1985) pp 802806.Google Scholar
14.National Bureau Standards, “Enhancing Robot Performance Measurement” The Industrial Robot, (NBS Tracking Laser Interfereometer), 5254 (03, 1986).Google Scholar
15.Adachi, T. and Mita, T., “Detecting Methods for the Position/direction of a Robot Hand using PSD and its ApplicationsAdvanced Robotics 1, No. 4, 357370 (1987).Google Scholar
16.Lau, K., Hocken, R. and Haynes, L., “Robot Performance Measurements Using Automatic Laser Tracking TechniquesRobotics and Computer-Integrated Manufacturing 2, No. 3/4, 227236 (1985).Google Scholar
17.Puskorius, G. V. and Feldkamp, L. A., “Global Calibration of a Robot/Vision System” IEEE, 190195 (1987).Google Scholar
18.King, F. G. and Puskorius, G. V., Yuan, F., Meier, R. C., Jeyabalan, V. and Feldkamp, L. A., “Vision Guided Robots for Automated Assembly” Proceedings IEEE International Conference Robotics and Automation,Philadelphia, Penn.(April 24–29, 1988) pp. 16111616.Google Scholar
19. NASA Tech Briefs, KSC-11447 (17 pages), “Robotic Target-Tracking Subsystem” (John F. Kennedy Space Center, Kennedy Space Center, Florida 32899).Google Scholar
20.Preising, B. M., “Computer Vision Based Robot Calibration and Control” Ph.D. Dissertation (Graduate Group Biomedical Engineering, University of California, Davis, Fall 1990).Google Scholar
21.Zaremba, M. B., “Robot Target Tracking System” The Changing Face of Manufacturing: Proceedings 9th Annual British Robot Association Conference,Stratford-upon-Avon, U.K.(May 13–14, 1986) pp. 225232.Google Scholar
22.Day, C. P., “Robot Accuracy Issues and Methods of Improvement” Robots 11–17th Int. Symposium Industrial Robots Conf. Proc.,Chicago, Illinois.(April 26–30, 1987) pp. 5–1 to 523.Google Scholar
23.ASTM Standards, Section 14:, General Methods and Instrumentation Volume 14.01, F 1034–86, 420427 (1989).Google Scholar
24.Colson, J. C. and Perreira, N. D., “Quasi-static Performance of robotsRobotics and Computer Integrated Manufacturing 2, No. 3/4, 261278 (1985).Google Scholar
25.Paul, R. P., Robot Manipulators: Mathematics, Programming, and Control (MIT Press, Cambridge, Mass., (1986) pp. 85118.Google Scholar
26.Imaging Technologies Incorporated, 600 West Cummings Partk, Woburn, MA. 01801.Google Scholar
27.Nackman, L. R., Lavin, M. A., Taylor, R. H. and Dietrich, W. C. JrAML/X User's Manual (Automation Research, Manufacturing Research Department, IBM Thomas J. Watson Research Center, Yorktown Heights, New York 10598. 1986).Google Scholar
28.Tsai, R. Y., “An efficient and accurate camera calibration technique for 3D machine vision” Proc. IEEE Computer Vision and Pattern Recognition(1986) pp. 364374.Google Scholar
29.Lenz, R. K. and Tsai, R. Y., “Techniques for calibration of the scale factor and image center for high accuracy 3D machine vision metrology” Proc. International IEEE Conference on Robotics and Automation(1987) pp. 14191428.Google Scholar
30.JKL Components Corporation, 13343 Paxton St., Pacoima California, 91331.Google Scholar
31.Sydenham, P. H.,Measureing Instruments: Tools of Knowledge and Control (Peter Peregrinus, Ltd., London, 1979) pp. 4649.Google Scholar
32.Daedal Incorporated, Harrison City, Pennsylvania, 15636.Google Scholar
33.Bowman, M. E. and Forrest, A. K., “Transformation Calibration of a Camera Mounted on a RobotImage and Vision Computing Vol 5, No. 4, 261266 (1987).Google Scholar
34.Shiu, Y. C. and Ahmad, S., “Calibration of Wrist-Mounted Robotic Sensors by Solving Homogeneous Transform Equations of the Form AX = XBIEEE Transactions on Robotics and Automation 5, No. 1, 1629 (02, 1989).Google Scholar
35.Tsai, R. Y. and Lenz, R. K., “A New Technique for Fully Autonomous and Efficient 3D Robotics Hand/Eye CalibrationIEEE Trans. Robotics Automation 5, No. 3, 345358 (06, 1989).Google Scholar
36.Chou, J. C. K. and Kamel, M., “Quaternions Approach to Solve the Kinematic Equation of Rotation, AaAx = AxAb of a Sensor-mounted Robot Manipulator” Proc. IEEE Int. Conf. Robotics and Automation,Philadelphia, Penn.(April 24–29, 1988) pp. 656662.Google Scholar
37.Funda, J. and Paul, R. P., “A Comparison of Transforms and Quaternions in Robotics” Proc. IEEE Int. Conf. Robotics and Automation,Philadelphia, Penn.(April 24–29, 1988) pp. 886891.Google Scholar
38.Mooring, B. W. and Pack, T. J., “Robot Calibration in an Industrial EnvironmentInt. J. Advanced Manufacturing Technology 3(5), 316 (1988).Google Scholar
39.Roth, Z. S., Mooring, B. W. and Ravani, B., “An Overview of Robot CalibrationIEEE J. Robotics and Automation RA-3, No. 5, 377384 (1987).Google Scholar
40.Chen, J. and Chao, L., “Positioninf Error Analysis for Robot Manipulators with All Rotary JointsIEEE J. Robotics and Automation RA-3, No. 6, 539545 (12. 1987).Google Scholar
41.Hollerbach, J. M., Annotated Bibliography for Kinematic Calibration (MIT Artificial Intelligence Lab, 5435 Technology Square, Cambridge, MA 02139, 07 7, 1987).Google Scholar