Parametric Selection of Industrial Robots Using Reduced PCR/PLSR Models for Better Estimates of Expected Cost and Specifications

Abstract

Quick advancement of industrial robots along with its usage by the assembling industries for various applications is a basic assignment for the determination of robots. As an outcome, the choice procedure of the robot turns out to be particularly entangled for the potential users since they have a broad arrangement of parameters of the accessible robots. In this paper, Partial Least Square Regression (PLSR) and Principal Component Regression (PCR) algorithm are utilized for the selection of industrial robots. In this proposed technique, eleven different parameters are taken as direct inputs for selecting a robot as compared to those of the existing models, which are limited up to seven parameters. Basing upon the proposed algorithm, the rank of an industrial robot is estimated. Moreover, the best robot that has been selected should satisfy the benchmark genuinity for a targeted application. In addition to this, the robot selection algorithm is measured through Mean Square Error (MSE), and Root Mean Square Error (RMSE), R-squared error(RSE).