esearch and application of rapid convergence has important significance and value for the trajectory planning of articulated robots.Smoothness of the trajectory and the time used by the articulated robot for different actions were taken as main optimization objectives, a fast convergent particle swarm optimization algorithm was proposed.The global learning factor and the local learning factor were combined,and a faster convergence rate was obtained by adjusting the proportion coefficient and the proportion of global learning factor.The experimental results show that the application effect of the proposed method is close to that of the improved particle swarm optimization algorithm when the number of iterations is relatively small; the proposed method is better than the improved particle swarm optimization algorithm when the number of iterations is large