Forward Kinematics Solution of Stewart-Gough using Improved Hybrid Strategy (Neural Network and 3rd-order Newton-Raphson)

Many efforts have been done in recent years to decrease the required time for analysis of FKP (Forward Kinematics Problem) of parallel robots.This paper starts with developing kinematics of a parallel robot and finishes with a suggested algorithm to solve forward kinematics of robots. In this paper, by combining the artificial neural networks and a 3rd-order numerical algorithm, an improved hybrid strategy is proposed in order to increase the accuracy and speed of forward kinematics analysis of parallel manipulators. First, an approximate solution of the forward kinematics problem is produced by the neural network. This approximate solution is then considered as the initial guess for the 3rd order Newton-Raphson numerical technique. By applying Stewart-Gough parallel manipulator, the efficiency of the proposed method is evaluated. It is shown that replacing the Newton-Raphson algorithm by the 3rd-order one leads to a reduction of the iterations required to reach the desired accuracy level and thus a reduction of the FKP analysis time. Finally, Stewart robot is used to simulate the movement of jaw. This novel algorithm can be applied to any forward kinematics of serial or parallel robots.