یک روش ترکیبی جدید برای مدلسازی سینماتیک مستقیم روباتهای موازی

Abstract

Unlike serial manipulators, forward kinematic problem (FKP) of parallel robots is highly complicated and its analytical solution is not generally le. Therefore, in most cases numerical methods are used to solve this problem which are relatively time consuming. On the other hand, reducing the duration of FKP analysis is an essential task for applications like real-time control. In this paper, artificial neural networks and a 3rd-order numerical algorithm are combined and a new hybrid strategy is proposed for forward kinematics analysis of parallel manipulators which significantly increases the speed of the FKP solution. In the proposed method, an approximate solution of the FKP is first produced by the neural network. This solution is next considered as an initial guess for the 3rd-order numerical technique which solves the nonlinear forward kinematics equations and obtains the answer with a desired level of accuracy. The proposed method is applied to a spatial 3-PSP parallel manipulator. The results show that using this method will lead to a 35 percent reduction in number of iterations and a 12 percent reduction in the FKP analysis time, while maintaining high level of solution accuracy.