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Design optimization of a cable-based parallel tracking system by using evolutionary algorithms

Published online by Cambridge University Press:  05 March 2014

Eusebio E. Hernandez ,
S.-I. Valdez ,
M. Ceccarelli ,
A. Hernandez  and
S. Botello
Eusebio E. Hernandez*
Affiliation:
National Polytechnic Institute, ESIME-UPT, Section of Graduate Studies and Research, Mexico City, Mexico
S.-I. Valdez
Affiliation:
Center for Research in Mathematics (CIMAT), Department of Computer Science, Guanajuato City, Mexico
M. Ceccarelli
Affiliation:
Laboratory of Robotics and Mechatronics, University of Cassino, Cassino (Fr), Italy
A. Hernandez
Affiliation:
Center for Research in Mathematics (CIMAT), Department of Computer Science, Guanajuato City, Mexico
S. Botello
Affiliation:
Center for Research in Mathematics (CIMAT), Department of Computer Science, Guanajuato City, Mexico
*
*Corresponding author. E-mail: euhernandezm@ipn.mx
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Summary

In this paper, an optimization design of a 6 DOF parallel measuring system is analyzed. First, a closed form direct kinematics formulation based on Cayley–Menger determinants is considered in the objective function, in order to measure the manipulator singularities, then an estimation of distribution algorithm is proposed to solve the optimization problem. It is shown that the evolutionary algorithm can find close to optimal solutions for minimum pose error estimation. Additionally, these global optimizers significantly reduce the computational burden in comparison with exhaustive search and other global optimization techniques. The sensitivity of the pose error estimation in the prescribed robots' workspace is analyzed and used to guide a designer in choosing the best structural configuration. Numerical examples are discussed to show the feasibility of the proposed optimization methodology.

Keywords

RoboticsParallel mechanism designTracking systemOptimizationEstimation of distribution algorithms
Type
Articles
Information
Robotica , Volume 33 , Issue 3 , March 2015 , pp. 599 - 610
Copyright
Copyright © Cambridge University Press 2014 

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