Multi-Objective Design of Parallel Manipulator Using Global Indices
F. A. Lara-Molina*, 1, J. M. Rosario1, D. Dumur2
Identifiers and Pagination:Year: 2010
First Page: 37
Last Page: 47
Publisher Id: TOMEJ-4-37
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
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.
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.