Comparison of numerical and neural network methods for the kinematic modeling of a hybrid structure robot

Abstract

A combination of parallel and serial mechanisms allows for stiff and dexterous motion of the robotic end-effector but increases the complexity of the kinematic problem. Many of the geometric parameters for such robots are difficult to obtain. This paper presents the numerical and neural network methods to solve the kinematics for such a hybrid robot named Exechon®. Both methods avoid the geometric measurement in the real robot. The geometric parameters used in the numerical model are identified by a particle swarm optimization algorithm. At the same time, a radial basis function-based neural network is trained to approximate the kinematics of the Exechon robot. The resultant models are then compared in terms of modeling accuracy and real-time ability. The presented methods are generic and can be applied to other robots with similar structures.