Positioning failure error identification of industrial robots based on particle swarm optimization and Kriging surrogate modeling
Wanghao Shen, Guojun Liu, Jialong He, Guofa Li, Liangsheng Han
Abstract
Abstract To tackle the low identification accuracy of robot positioning, this paper proposes a positioning failure error identification method for industrial robots, combining the Kriging surrogate model and the improved particle swarm optimization algorithm. First, based on the Denavit‐Hartenberg and the small displacement screw methods, the kinematic model of a six‐degree‐of‐freedom industrial robot with joint errors is constructed. Then, the Kriging surrogate model of the kinematic error is constructed, which is trained by generated training samples. Finally, the joint error identification of industrial robots is solved by an improved particle swarm optimization algorithm based on exponential inertia weight and sub‐population cooperation under the multi‐attitude positioning condition. The simulation results show that the proposed optimization algorithm can significantly improve the convergence accuracy and accurately identify the actual errors at each joint of the industrial robot.