Comparative Study of Optimization Technique for the Global Performance Indices of the Robot Manipulator Based on an Approximate Model

Abstract

This paper presents the procedure and results of the multi-objective design optimization of a seven-degrees-of-freedom (7DOF) robot manipulator for better global performance, which pertains to the Global Conditioning Index (GCI) and the Structural Length Index (SLI). The concepts of, and the calculation techniques for, GCI and SLI are introduced to allow their use as objective functions for optimization. The optimization techniques, which are Sequential Two-point Diagonal Quadratic Approximate Optimization (STDQAO), the Progressive Quadratic Response Surface Method (PQRSM), the micro genetic algorithm (mu GA), and the evolutionary algorithm (EA), were explained briefly, and they are being used to optimize the global performance indices of the robot manipulator. Also, the results of the optimization and comparison of the four optimization methods are summarized in tables.