Efficient hybrid method for forward kinematics analysis of parallel robots based on signal decomposition and reconstruction

• Main Authors: Qiming Wang, Jian Su, Zhichao Lv, Lan Zhang, Huiying Lin, Guan Xu
• Format: Article
• Language: English
• Published: SAGE Publishing 2017-05-01
• Series: Advances in Mechanical Engineering
• Online Access: https://doi.org/10.1177/1687814017699094

This article combines a new method based on signal decomposition and reconstruction with a fifth-order numerical algorithm and proposes an efficient hybrid method for solving forward kinematics problem of parallel manipulators. In this hybrid method, new method can first generate an approximate solution of the forward kinematics problem, which will be taken as initial guess for the fifth-order numerical technique. The answer with desired level of accuracy is then obtained. The superposition principle is proposed stating that each rod’s displacement required to drive the mobile platform with 6-degree-of-freedom coupled motion is approximately a sum of the rod’s displacement required to drive it with each of six single basic motions including roll q 1 , pitch q 2 , yaw q 3 , surge q 4 , sway q 5 , and heave q 6 . By solving the equations established in accordance with the superposition principle, the approximate solution is also gained. Superposition principle is more applicable to the robots with small rotational workspace. The solution is directly solved under superposition principle. Even though the rotational workspace is expanded, the solution still meets the required accuracy of initial value of high-order iterative technique for obtaining its exact solution. The proposed method is then applied to a test bench for bogie parameters and a Stewart platform. Simulation results demonstrate that maximum absolute error of displacement along X-, Y-, and Z-axis is 0.174 mm and its relative error is about 3.4‰. Using this method will reduce about 34.1% of the required number of iterations to solve the forward kinematics problem compared with improved hybrid method and up to 49.8% compared with the Newton–Raphson method.