The calibration scheme of robot forward kinematics presented in this paper has a number of features. Firstly, robot kinematic errors are modeled in a recursive format and as such, the number of measurements that need to be taken for calibration can be determined by studying the rate of convergence of estimation error covariance. Secondly, a simplified adaptive filtering algorithm is used to deal with unknown measurement noise statistics and unknown robot motion repeatability characteristics in estimating the kinematic errors. Thirdly, a laser interferometry system is used to measure positions of a robot end-effector in world coordinates. The measurement system was implemented in experiments involving a three degree-of-freedom gantry robot. The adaptive filtering of the experimental data identified 0.5 to 1.5 percent errors in representative kinematic parameters of the given robot by taking into account measurement noise and robot repeatability.

Issue Section:
Technical Papers
Topics:
Calibration, Filtration, Manipulators, Robots, Errors, Kinematics, Noise (Sound), Algorithms, Degrees of freedom, End effectors, Interferometry, Lasers, Measurement systems, Robot motion, Statistics
1.
Denavit, J., and Hartenberg, R. S., 1955, “A Kinematic Notation for Lower-Pair Mechanisms based on Matrices,” ASME Journal Applied Mechanics, June, pp. 215–221.
2.
Wu
C. H.
,
1984
, “
A Kinematic CAD Tool for the Design and Control of a Robot Manipulator
,”
Int. J. of Robotics Research
, Vol.
3
, No.
1
, Spring, pp.
58
–
67
.
3.
Wu
C. H.
, and
Lee
C. C.
,
1985
, “
Estimation of the Accuracy of a Robot Manipulator
,” Short Paper,
IEEE Trans. Automatic Control
, Vol.
AC-30
, No.
3
, March, pp.
304
–
305
.
4.
Veitschegger
W. K.
, and
Wu
C. H.
,
1986
, “
Robot Accuracy Analysis Based on Kinematics
,”
IEEE J. of Robotics and Automation
, Vol.
RA-2
, No.
3
, pp.
171
–
179
.
5.
Whitney
D. E.
,
Lazinski
C. A.
, and
Rourke
J. M.
,
1986
, “
Industrial Robot Forward Calibration Method and Results
,”
ASME Journal of Dynamic Systems, Measurement, and Control
, Vol.
108
, March, pp.
1
–
8
.
6.
Stone, H. W., Sanderson, A. C., and Newman, C. P., 1986, “Arm Signature Identification,” Proc. IEEE Int. Conf. on Robotics and Automation, pp. 41–48.
7.
Mukerjee, C. P., and Khosla, P. K., 1985, “Self-Calibration in Robot Manipulators,” Proc. IEEE Int. Conf. on Robotics and Automation, pp. 1050–1057.
8.
Hayati, S. A., and Mirmirani, M., 1984, “A Software for Robot Geometry Parameter Estimation,” SME Paper #MS84-1052.
9.
Mehra
R. K.
,
1970
, “
On the Identification of Variances and Adaptive Kalman Filtering
,”
IEEE Trans. Automatic Control
, Vol.
AC-15
, No.
2
, April, pp.
175
–
184
.
10.
Yao
Y. L.
, and
Mohd Yusoff
M. R.
,
1992
, “
A CAD Based Error Mapping and Layout Facilities for Precision Robotic Operations
,”
Robotics and Computer Integrated Manufacturing
, Vol.
9
, No.
6
, pp.
505
–
385
.
11.
Kalman
R. E.
,
1960
, “
A New Approach to Linear Filtering and Prediction Problems
,”
ASME Transactions Ser. D. J. Basic Engineering
, Vol.
82
, pp.
34
–
45
.
12.
Nishimura
T.
,
1967
, “
Error Bounds of Continuous Kalman Filters and the Application to Orbit Determination Problems
,”
IEEE Trans. Automatic Control
, Vol.
AC-12
, pp.
268
–
275
.
13.
Goodwin, G. C., and Sin, K. S., 1984, Adaptive Filtering, Prediction and Control, Prentice Hall.
14.
Graupe, D., 1984, Time Series Analysis, Identification and Adaptive Filtering, Robert E. Krieger Publishing Company.
15.
Roth
Z. S.
,
Mooring
B. W.
, and
Ravani
B.
,
1987
, “
An Overview of Robot Calibration
,”
IEEE J. Robotics and Automation
, Vol.
RA-3
, No.
5
, pp.
377
–
385
.
16.
IBM 7565 Manufacturing System Operation and Service Manual, 1985.
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