Optimal manipulator parameter tolerance selection using evolutionary optimization technique

Abstract

Robot system designers often face the challenge of selecting optimal parameter tolerances of a manipulator, which delivers optimal performance. This paper presents an approach to simulate the performance of manipulator and evolutionary optimization method to select optimal parameter tolerance. To determine optimal parameter tolerance, genetic algorithm, and differential evolution, optimization techniques have been used. The objective function maximizes SN Ratio, while manipulator performs a task. As differential evolution and GA are best suited for solving deterministic optimization problems, to handle performance of manipulator, a hybrid technique is proposed. The evolutionary optimization techniques are coupled with orthogonal array used in the Taguchi method to get optimal solution. The hybrid technique is illustrated by an example and concluded that it is best suited for manipulator parameter tolerance design. It is also observed that differential evolution technique converges quickly and require significantly less number of functional evaluations.