论文:2013,Vol:31,Issue(6):901-907
引用本文:
唐必伟, 朱战霞, 方群, 陈攀峰. 基于改进蚁群算法的无人驾驶飞行器三维航迹规划与重规划[J]. 西北工业大学
Tang Biwei, Zhu Zhanxia, Fang Qun, Chen Panfeng. Planning and Replanning 3D Route of UAV Using Improved Ant Colony Algorithm[J]. Northwestern polytechnical university

基于改进蚁群算法的无人驾驶飞行器三维航迹规划与重规划
唐必伟1,2, 朱战霞1,2, 方群1,2, 陈攀峰3
1. 西北工业大学 航天学院, 陕西 西安 710072;
2. 西北工业大学 航天飞行动力学技术国家级重点实验室, 陕西 西安 710072;
3. 湖北三江航天红峰控制公司, 湖北 孝感 432900
摘要:
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。
关键词:    改进蚁群算法    三维航迹规划    三维航迹重规划    动态窗口    最小威胁曲面   
Planning and Replanning 3D Route of UAV Using Improved Ant Colony Algorithm
Tang Biwei1,2, Zhu Zhanxia1,2, Fang Qun1,2, Chen Panfeng3
1. College of Astronautics, Northwestern Polytechnical University Xi'an 710072, China;
2. Science and Technology on Aerospace Flight Dynamics Laboratory Xi'an 710072, China;
3. Hubei Sanjiang Space Hongfeng Control Equipment Co. Ltd., Xiaogan 432900, China
Abstract:
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.
Key words:    algorithms    collision avoidance    cost functions    efficiency    global optimization    mathematical models    MATLAB    probability    three dimensional    two dimensional    unmanned aerial vehicles (UAV)    improved ant colony algorithm    3D route planning    3D route replanning    dynamic window   
收稿日期: 2012-12-04     修回日期:
DOI:
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作者简介: 唐必伟(1987-),西北工业大学博士研究生,主要从事无人驾驶飞行器航迹规划研究。
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