改进的蚁群算法在无人机航路规划中的应用

蚁群算法是一种新的源于生物界的仿生随机优化方法。简单介绍了无人机航路规划的基本步骤。针对基本蚁群算法的4个不足,提出了新的改进算法。在算法中设定具体目标和可能经过的威胁点,在信息激素中除了有距离信息还需要增加威胁度信息,并将威胁度设为权重较高的参数指标,在航路规划仿真开始阶段,同时发送多个探路人工蚁。信息激素中的信息是随时更新的,以便于适应战场动态变化。利用MATLAB仿真运算验证了改进算法的有效性。

Application of Improved Ant Colony Algorithm in Path Planning of Unmanned Aircraft System

Ant Algorithm is a bionic stochastic optimization method coming from living nature.The basic steps of the UAS(Unmanned Aircraft System) path planning are introduced.After analyzing four insufficiencies of theory of the basic ant algorithm,a new improved algorithm is discussed.Firstly,the object position and threat position that UAS passes through when executing battle mission are set up.Secondly,the content of pheromone must have the information of distance and the information of degree of threat which is set as upper parameter.Thirdly,at the beginning of simulation,several manpower ants which are special exploring road ants are sent simultaneously.Finally,the content of pheromone for selecting UAS path is dynamic.The MATLAB simulation results show that the improved ant colony algorithm for path planning of UAV is effective.