Robotica
Robotica, FirstView Article : pp 1-15
Copyright © Cambridge University Press 2011
DOI: 10.1017/S0263574711001032 (About DOI)
Published online: 30 September 2011
Articles
Multiobjective optimization of parallel kinematic mechanisms by the genetic algorithms
Ridha Kelaiaiaa1a2 c1, Olivier Companya2 and Abdelouahab Zaatria3
SUMMARY
It is well known that Parallel Kinematic Mechanisms (PKMs) have an intrinsic dynamic potential (very high speed and acceleration) with high precision and high stiffness. Nevertheless, the choice of optimal dimensions that provide the best performances remains a difficult task, since performances strongly depend on dimensions. On the other hand, there are many criteria of performance that must be taken into account for dimensional synthesis, and which are sometimes antagonist. This paper presents an approach of multiobjective optimization for PKMs that takes into account several criteria of performance simultaneously that have a direct impact on the dimensional synthesis of PKMs. We first present some criteria of performance such as the workspace, transmission speeds, stiffness, dexterity, precision, as well as dynamic dexterity. Secondly, we present the problem of dimensional synthesis, which will be defined as a multiobjective optimization problem. The method of genetic algorithms is used to solve this type of multiobjective optimization problem by means of NSGA-II and SPEA-II algorithms. Finally, based on a linear Delta architecture, we present an illustrative application of this methodology to a 3-axis machine tool in the context of manufacturing of automotive parts.
(Accepted August 25 2011)
KEYWORDS:
Parallel robots;
Parallel kinematic machine tool;
Criteria of performance;
Multiobjective optimization;
Genetic algorithms NSGA-II and SPEA-II
Subject: a Publication in Robotica Journal 
Sat 12 Nov - 2:14
