Trajectory planning technology is the key to ensure the UAV completing its mission successfully.An improved ant colony algorithm is proposed to address the problems of the traditional ant colony algorithm,such as being prone to local optimum and poor convergence performance in UAV trajectory planning.Firstly, the initial pheromone concentration is unevenly distributed. A relatively good trajectory is obtained by using A* algorithm,and the blindness of initial search is avoided by making the pheromone concentration on this trajectory higher than that on other trajectories. Secondly,a heuristic factor is introduced to optimize the state transition rules,which improves the convergence rate of the algorithm.Finally,the variation of pheromone volatilization factor is smoothed by using the characteristics of Gaussian function,and the pheromone volatilization factor can be dynamically adjusted according to the distance between the current state and the target,thus avoiding the situation that the algorithm may fall into local optimum.The simulation results show that the proposed algorithm generates trajectories with shorter lengths and converges faster than the traditional ant colony algorithm does in the same environment.