Real-time Pose Measurement of Parallel Robot Based on GRNN

Abstract

The real-time pose measurement of parallel robot helps to achieve the closed loop pose control and improve the control and operating performance of parallel robot. But it is difficult to implement the real-time pose measurement directly. In order to solve the pose measurement problem of a 6-DOF parallel robot, the kinematics analysis of the parallel robot is made, and a Generalized Regression Neural Network  which has fast convergence and strong nonlinear mapping ability is established by setting the desired pose and its inverse kinematics results as the neural network training samples to implement the map of parallel robot from the joint variable space to the work variable space. Finally, the real-time pose measurement of parallel robot is achieved by using the trained neural network and the actual motion states of the active joints easily detected. The simulation experiment results show that the method of measuring the parallel robot pose based on the GRNN has the faster convergence rate and higher measurement accuracy than those of the BPNN and RBFNN methods. The research establishes the basis for the direct closed control of parallel robot pose. DOI: http://dx.doi.org/10.11591/telkomnika.v11i5.2455