Global Visual-Motor Estimation for Uncalibrated Visual Servoing
Full Text: farahmand2007a.pdf
In this paper, we present two methods for the estimation of a globally valid visual-motor model of a robotic manipulator. In conventional uncalibrated visual servoing, the visuo-motor function is approximated locally with a Jacobian. However, for optimal task planning, or nonlinear controller design with global stability guarantee, one needs to know a model that provides some information about the behavior of the system over the whole workspace. Our presented methods remedy this drawback in uncalibrated visual servoing by incrementally building a global estimator based on the movement history. We implement two such methods. The first method is a K-nearest neighborhood regressor over Jacobian that uses previously estimated local models. The second method stores previous movements and computes an estimate of the Jacobian by solving a local least squares problem. Experimental results show that both methods provide better global estimation quality compared to the conventional local estimation method with much lower estimation variance.
Citation
A. Farahmand, A. Shademan, M. Jagersand. "Global Visual-Motor Estimation for Uncalibrated Visual Servoing". International Conference on Intelligent Robots and Systems, October 2007.| Keywords: | Machine Learning, Robotics, Vision |
| Category: | In Conference |
BibTeX
@incollection{Farahmand+al:IROS07,
author = {A.M. Farahmand and Azad Shademan and Martin Jagersand},
title = {Global Visual-Motor Estimation for Uncalibrated Visual Servoing},
booktitle = {International Conference on Intelligent Robots and Systems},
year = 2007,
}Last Updated: January 05, 2012Submitted by Azad Shademan
