Jixie chuandong (Feb 2019)
Kinematics Analysis of Grasping Manipulator based on ART-RBF Learning Algorithm
Abstract
In view of the difficulties encountered in the study of the kinematics inverse kinematics of grasping manipulator, a ART-RBF model based on soft competition mechanism is selected. On the basis of traditional RBF neural network, adaptive control generates the number of hidden layer nodes, and the similarity soft competition is applied in the first stage of learning. Using the soft competition mechanism, each node of the hidden layer can be involved in the learning of the sample, the utilization rate of the node is improved, and the error of the sample in the inter class overlap can be reduced, and the prediction accuracy can be improved. Finally, the motion simulation of the manipulator is carried out by ADAMS, and it is compared with the forward kinematics solution to verify the correctness of the positive solution equation. The results show that the soft competition algorithm can improve the prediction accuracy to a certain extent. The simulation results show the accuracy of the positive solution data and provide the basis for the follow-up motion control.
