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

David Daney 1 Yves Papegay 1 Blaise Madeline 1 
1 COPRIN - Constraints solving, optimization and robust interval analysis
CRISAM - Inria Sophia Antipolis - Méditerranée , ENPC - École des Ponts ParisTech
Abstract : 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.
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Submitted on : Thursday, November 21, 2013 - 4:56:05 PM
Last modification on : Friday, February 4, 2022 - 3:18:27 AM

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David Daney, Yves Papegay, Blaise Madeline. Choosing Measurement Poses for Robot Calibration with the Local Convergence Method and Tabu Search. The International Journal of Robotics Research, 2005, 24 (6), pp.501--518. ⟨10.1177/0278364905053185⟩. ⟨hal-00907749⟩



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