Choosing Measurement Poses for Robot Calibration with the Local Convergence Method and Tabu Search - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Journal Articles The International Journal of Robotics Research Year : 2005

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

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.

Dates and versions

hal-00907749 , version 1 (21-11-2013)

Identifiers

Cite

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⟩
113 View
0 Download

Altmetric

Share

Gmail Facebook X LinkedIn More