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The International Journal of Robotics Research, Vol. 24, No. 6, 501-518 (2005)
DOI: 10.1177/0278364905053185
© 2005 SAGE Publications

Choosing Measurement Poses for Robot Calibration with the Local Convergence Method and Tabu Search

David Daney

COPRIN Team, Sophia Antipolis Research Unit, French National Institute for Computer Science and Control, BP 93, 06902 Sophia Antipolis Cedex, France

Yves Papegay

COPRIN Team, Sophia Antipolis Research Unit, French National Institute for Computer Science and Control, BP 93, 06902 Sophia Antipolis Cedex, France, yves.papegay{at}sophia.inria.fr

Blaise Madeline

IMERIR Engineering School of Robotic and Computer Science, BP 2013, 66011 Perpignan Cedex, France

The robustness of robot calibration with respect to sensor noise is sensitive to the manipulator poses used to collect measurement data. In this paper we propose an algorithm based on a constrained optimization method, which allows us to choose a set of measurement configurations. It works by selecting iteratively one pose after another inside the workspace. After a few steps, a set of configurations is obtained, which maximizes an index of observability associated with the identification Jacobian. This algorithm has been shown, in a former work, to be sensitive to local minima. This is why we propose here meta-heuristic methods to decrease this sensibility of our algorithm. Finally, a validation through the simulation of a calibration experience shows that using selected configurations significantly improve the kinematic parameter identification by dividing by 10-15 the noise associated with the results. Also, we present an application to the calibration of a parallel robot with a vision-based measurement device.

Key Words: robust design • experimentation • calibration • parallel robot • local convergence • Tabu search


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