Stiffness performance optimization method of drilling robot based on QPSO algorithm

Due to its high flexibility and intelligence, industrial robots are being increasingly used in aeronautical processing tasks. However, the inherently low stiffness of robots seriously affects the processing quality of their operations. Studying its stiffness characteristics and optimization methods is an effective method to improve the stiffness performance of robot. This paper proposes a robot posture optimization method based on quantum particle swarm algorithm to solve the problem of vibration during drilling. Firstly, the kinematic model and static stiffness model of the robot are established, and then a stiffness index oriented to the machining plane is proposed. Next, a new performance evaluation index is obtained by normalization fusion with manipulability to quantitatively evaluate the stiffness performance of the robot. After this, the drilling posture is optimized by quantum particle swarm optimization algorithm under the boundary constraints and property constraints of the robot. Finally, drilling experiments were carried out on AUBO i5 robot to verify the accuracy of the performance evaluation coefficient and optimization method. Compared with non-optimization and particle swarm optimization, the robot performance was improved by 32.28% and 8.57% on average, indicating that this method can effectively improve the robot stiffness performance.