Abstract
This paper presents a novel redundantly actuated 2RPU-2SPR parallel manipulator that can be employed to form a five-axis hybrid kinematic machine tool for large heterogeneous complex structural component machining in aerospace field. On the contrary to series manipulators, the parallel manipulator has the potential merits of high stiffness, high speed, excellent dynamic performance, and complicated surface processing capability. First, by resorting to the screw theory, the degree of freedom of the proposed parallel manipulator is briefly addressed with general configuration and verified by Grübler-Kutzbach (G-K) criteria as well. Next, the inverse kinematics solution for such manipulator is deduced in detail; however, the forward kinematics is mathematically intractable. To deal with such problem, the forward kinematics is solved by means of three back propagation (BP) neural network optimization strategies. The remarkable simulation results of the parallel manipulator demonstrate that the BP neural network with position compensation is an appropriate method for solving the forward kinematics because of its various advantages, such as high efficiency and high convergence ratios. Simultaneously, workspaces, including joint space and workspace of the proposed parallel manipulator, are graphically depicted based on the previous research, which illustrate that the proposed manipulator is a good candidate for engineering practical application.
Highlights
Various types of mechanisms have been applied in many robotic fields
Karimi and Nategh [13] utilized a statistical approach including Bates and Watts to investigate the nonlinearity of the forward kinematics, and the results demonstrate that the length of the region has a significant impact on the nonlinearity of the parallel manipulator
back propagation (BP) neural network, and the 3rd represents the BP neural network with position compensation), we found that the BP neural network with position compensation has the best effect for the forward kinematics solution of the parallel manipulator
Summary
Various types of mechanisms have been applied in many robotic fields. Nowadays, some parallel manipulators have attracted exhaustive attention from both academia and industry. Tsai et al [12] employed Bezou’s elimination method and optimization techniques to solve the forward kinematics problem of the 3-PRS parallel mechanism, which makes it possible for real-time control applications. Wang et al [15] proposed a novel forward kinematics algorithm, which is verity in the 3-RPS parallel mechanism, and the accuracy is, less than 10-6, sufficiently high It needs to select a reasonable initial value. Neural networks are employed to solve the forward kinematics of a parallel manipulator and draw particular interests for numerous researchers, due to their considerable ability to approximate nonlinear mapping functions. The goal of this paper is to solve the forward kinematics, which promotes further practical applications of the promising redundantly actuated 2RPU-2SPR parallel manipulator. Some concluding remarks are provided in the last section
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
