基于改进ACO算法的无人作战飞机航路规划设计  

Design of Path Planning for UCAV Based on Improved ACO Algorism

作　　者：庄夏[1] 戴敏[2] 贺元骅[3] 

机构地区：[1]中国民航飞行学院科研处,四川广汉618307 [2]中国民航飞行学院计算机学院,四川广汉618307 [3]中国民航飞行学院航空安全保卫学院,四川广汉618307

出　　处：《计算机测量与控制》2014年第1期270-272,共3页Computer Measurement &Control

基　　金：省教育厅项目(13ZB0352);国家自然科学基金资助项目(61079022)

摘　　要：为了使无人作战飞机能在敌方防区内以最小的发现概率和航路代价到达目标点,提出一种基于改进蚁群算法对UCAV进行航路规划的方法;首先,根据燃油代价和威胁代价设计了目标函数,并根据敌方防区威胁源生成VORONOI图,然后通过对信息素初始化方式、蚂蚁转移规则以及信息素更新规则进行改进以优化传统的ACO算法,最后定义了使用改进ACO算法对UCAV进行航路规划的具体算法;仿真实验证明文中方法能实现UCAV航路规划,且与经典ACO算法相比,文中方法具有较快的收敛速度和较强的全局寻优能力。For making UCAV （Unmanned Combat Aerial Vehicle） reaching the goal in the enemy district with the minimum detection probability and path planning, a method based on improved ACO （Ant Colony Optimization） algorism was proposed. Firstly, the goal function was designed according to the threat cost and fuel cost, and the VORONOI diagram was generated by threat source in enemy district. Then the pheromone initialization, transferring rules for ant and the renew rules for path were improved to optimize the traditional ACO algorism. Finally, the improved ACO algorism for planning the path for UCAV was defined. The simulation experiment shows our method can realize the path planning, and compared with the traditional ACO algorism, it has the advantages of rapid convergence and global optimizing ability.

关 键 词：无人作战飞机 VORONOI图 蚁群算法 航路 

分 类 号：TP391.1[自动化与计算机技术—计算机应用技术]