A step identification method of joint parameters of robots based on the measured pose of end-effector

Abstract

According to the measured pose error of end-effector, a step identification method of joint parameters based on quantum-behaved particle swarm optimization algorithm is proposed to improve the accuracy of robots. Due to the nonlinear characteristic of kinematic model of robots, the identification problem of joint parameters is regarded as a nonlinear optimization problem, and solved through the two-step identification. Firstly, the joint parameters are individually optimized in the convergence order, and the prior converged joint parameter is substituted into optimization model to continue iteration until all of the joint parameters are converged. And secondly, the joint parameters are further optimized simultaneously in the searching space around previous converged values to finish the kinematic identification. The simulation results illustrate that not only the identification accuracy, but the identification efficiency can be improved by adopting this method. Furthermore, the step identification method of joint parameters is feasible for both serial robots and parallel robots.