Comparison between PCA and KPCA methods in comprehensive evaluation of robotic kinematic dexterity

Due to the correlation and diversity of robotic kinematic dexterity indexes,the principal component analysis(PCA) and kernel principal component analysis(KPCA) based on Unear dimension reduction and nonlinear dimension reduction principle could be respectively introduced into comprehensive kinematic dexterity performance evaluation of space 3R robot of different tasks.By comparing different dimension reduction effects,the KPCA method could deal more effectively with the nonlinear relationship among different single kinematic dexterity indexes,and its calculation result is more reasonable for containing more comprehensive information.KPCA’s calculation provides scientific basis for optimum order of robotic tasks,and furthermore a new optimization method for robotic task selection is proposed based on various performance indexes.