Aiming at the problems of robot working efficiency and motion stability,a trajectory planning method based on improved hybrid particle swarm optimization algorithm was proposed.A new simulated annealing mechanism was established,and the multi-objective optimization was carried out with particle swarm optimization;nonlinear inertia weight decreasing strategy and dynamic learning factor were used to balance local and global search ability.Taking a 6 DOF robot (SFRTA) as the research object,the trajectory curve was constructed by using 5-7-5 interpolation polynomial;the optimal value was obtained by normalizing the objective function based on time pulsating impact by using weight coefficient method.The results show that compared with the traditional particle swarm optimization algorithm,by using the proposed algorithm,the running time and the pulsation impact of the manipulator can be reduced effectively, which has better stability.