This paper applies the improved ant colony algorithm to the planning of the 3D route of the UAV during its mission completion .To avoid the algorithm from easily falling into local optimum and early iterative stagnation , it puts forward a new method for pheromone volatility coefficient random self-adaptive adjustment .Its core consists of:(1) the minimum threat surface is projected horizontally and used to convert the 3D route planning into the 2D route planning;(2) the concept of dynamic window is used to improve the survival probability of the UAV in the complex battlefield environment when the UAV needs to replan its route because of the sudden appearance of un -known threats .The simulation results , given in Figs .2 through 9 and Tables 1 through 4, and their analysis show preliminarily that:(1) the new method based on the improved ant colony algorithm is superior to the method based on the basic ant colony algorithm because of its better solution and high speed ;( 2) the new method has strong a-daptability to the 3D route replanning of the UAV .%研究了一种改进蚁群算法在无人驾驶飞行器三维航迹规划中的应用。针对基本蚁群算法容易过早陷入局部最优以及过早陷入迭代停滞的缺陷，新提出了一种信息素挥发系数的随机自适应调节方法；借助最小威胁曲面这个概念，将最小威胁曲面向水平面投影，使三维航迹规划转换为二维航迹规划；并借助动态窗口这个概念，在三维离线航迹的基础上进行航迹局部重规划；最后给出仿真验证。仿真结果表明：改进蚁群算法在解的优越性和算法的快速性上都全面优于基本蚁群算法，并且改进的蚁群算法在三维航迹重规划上有很强的适应性。
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