Genetic Algorithms Multiobjective Optimization of a 2 DOF Micro Parallel Robot

Abstract

This paper is aimed at presenting a study on the optimization of the Bipod micro parallel robot, which comprises a two-degree-of-freedom (DOF) micro parallel robot with variable struts. The robot workspace is characterized and the inverse kinematics equation is obtained. In the paper, design optimization is implemented with genetic algorithms (GA) for optimization considering transmission quality index, manipulability, stiffness and workspace. Here, intended to show the advantages of using the GA, we applied it to a multicriteria optimization problem of 2 DOF micro parallel robot. Genetic algorithms (GA) are so far generally the best and most robust kind of evolutionary algorithms. A GA has a number of advantages. It can quickly scan a vast solution set. Bad proposals do not affect the end solution negatively as they are simply discarded. The obtained results have shown that the use of GA in such kind of optimization problem enhances the quality of the optimization outcome, providing a better and more realistic support for the decision maker.