REVIEW ARTICLE


Multi-Objective Design of Parallel Manipulator Using Global Indices



F. A. Lara-Molina*, 1, J. M. Rosario1, D. Dumur2
1 Department of Mechanical Design, Mechanical Engineering School, State University of Campinas, Campinas SP, Brazil
2 Department of Mechanical Design, Mechanical Engineering School, State University of Campinas, Campinas SP, Brazil

Article Information


Identifiers and Pagination:

Year: 2010
Volume: 4
First Page: 37
Last Page: 47
Publisher Id: TOMEJ-4-37
DOI: 10.2174/1874155X01004010037

Article History:

Received Date: 9/3/2010
Revision Received Date: 11/6/2010
Acceptance Date: 19/6/2010
Electronic publication date: 30/12/2010
Collection year: 2010

© 2010 Lara-Molina et al

open-access license: This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: (https://creativecommons.org/licenses/by/4.0/legalcode). This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

* Address correspondence to this author at the Departamento de Projeto Mecanico - Faculdade de Engenharia Mecanica - UNICAMP. Rua Mendeleiev, s/n - Cidade Universitaria “Zeferino Vaz” - Barao Geraldo - Caixa Postal 6122 - CEP: 13.083-970 - Campinas - SP, Brazil; Tel:+551935213166; Fax: +551932893722; E-mail: lara@fem.unicamp.br


Abstract

The paper addresses the optimal design of parallel manipulators based on multi-objective optimization. The objective functions used are: Global Conditioning Index (GCI), Global Payload Index (GPI), and Global Gradient Index (GGI). These indices are evaluated over a required workspace which is contained in the complete workspace of the parallel manipulator. The objective functions are optimized simultaneously to improve dexterity over a required workspace, since single optimization of an objective function may not ensure an acceptable design. A Multi-Objective Evolution Algorithm (MOEA) based on the Control Elitist Non-dominated Sorting Genetic Algorithm (CENSGA) is used to find the Pareto front.

Keywords: Parallel manipulator, optimal design, multi-objective genetic algorithm, stewart-gough platform.