Trajectory planning of a 6-DOF robot based on RBF neural networks

Abstract

A new method for smooth trajectory planning of a 6-DOF robot in joint space is described in this paper. By the researching processes of concrete analysis of trajectory planning on robot's manipulator arm, imitation of trajectory based on kinematics and optimization of trajectory in the joint space, an one-input-six-output RBF neural network model is built and trained taking the discrete time as input and the values of six angles as outputs in joint space. With character of rapid convergence and near approximation, this new algorithm is fault tolerant and irrelative with order of inputs, which can ensure the result trajectory is firing enough. The algorithm has been tested in simulation when the virtual model of the robot was established in software ADAMS, yielding good results by studying the kinematics and the dynamics performance of the robot.