Analysis of Efficient and Fast Prediction Method for the Kinematics Solution of the Steel Bar Grinding Robot

Abstract

Aiming at the robotization of the grinding process in the steel bar finishing process, the steel bar grinding robot can achieve the goal of fast, efficient, and accurate online grinding operation, a multi-layer forward propagating deep neural network (DNN) method is proposed to efficiently predict the kinematic solution of grinding robot. The process and kinematics model of the grinding robot are introduced. Based on the proposed method, simulations of the end position and orientation, and joint angle of the grinding robot are given. Three different methods, including SGD + tanh, Nadam + tanh, Nadam + ELU, are used to test the DNN calculation process results show that the method combining Nadam with ELU function has the fastest solution speed and higher accuracy can be obtained with the increase in iteration times. Finally, the Nadam optimizer is used to optimize the calculation results of the example. The optimization results show that this method accelerates the convergence rate of trajectory prediction error and improves the accuracy of trajectory prediction. Thus, the proposed method in this paper is an effective method to predict the kinematic solution when the grinding robot works online.