Pick position optimization of vacuum suction gripper frame in IRB360 robot using Taguchi analysis and genetic algorithm

Abstract

ABSTRACT Multi-Point Contact Vacuum Suction Grippers, coupled to an IRB 360 robot mechanical system, are commonly used for performing numerous pick and place operations on flat glass plates. The position of the suction cups over the pick-positions has a key role in defining the self-weight deformation of the flat glass plate when lifted. Now every robotic material handler produces its own inherent mechanical vibrations. In this work, a method is proposed for defining the optimal pick-positions for a flat glass plate of specific dimensions, with a view to minimise the deformation and accordingly the forced vibrations during robotic handling. The results of the above method were then used in designing a three-point vacuum gripper and perform pick and place operations. The above method involves a combination of Taguchi design of experimental techniques on finite element analysis, and genetic algorithm is used to optimise the Robot pick position. Regression analysis is used to develop the predicted model of robot pick position and ANOVA analysis is used to contribute the individual process parameter on robot pick position intervals. The developed mathematical model was further analysed with Genetic algorithm to find out the optimum conditions leading to the maximum pick position accuracy.