自适应蚁群算法的无人机三维航迹规划  

3D UAV Flight Path Planning with Adaptive Ant Colony Optimization

作　　者：张骜 毛海亮 卞鹏 陈侠 ZHANG Ao;MAO Hailiang;BIAN Peng;CHEN Xia(Shenyang Institute of Science and Technology,Shenyang 110000,China;Haiying Aviation General Equipment Co.Ltd.,Beijing 100000,China)

机构地区：[1]沈阳科技学院,沈阳110000 [2]海鹰航空通用装备有限责任公司,北京100000

出　　处：《电光与控制》2024年第5期24-29,共6页Electronics Optics & Control

基　　金：国家自然科学基金(61906125)。

摘　　要：针对传统蚁群算法三维空间节点多、算法搜索难度大等问题,提出自适应蚁群(IAACO)算法的无人机三维航迹规划算法。首先,通过栅格划分三维空间,使该算法可以应用于三维航迹规划;然后,建立一种不均匀的初始信息素矩阵,并加入一个自适应的信息素挥发因子,提高了算法的搜索效率,同时也加快了算法的收敛速度;最后,通过定义三维的长度指标函数和角度指标函数,进一步建立无人机航迹优化的目标函数,实现了三维航迹规划的全局优化。仿真结果表明,所提算法运行时间更短、收敛速度更快,规划出的航迹也更短更平滑。To solve the problems of traditional Ant Colony Optimization(ACO)such as too many nodes in three-dimensional space and difficult algorithm search,a UAV three-dimensional flight path planning algorithm based on Improved Adaptive Ant Colony Optimization(IAACO)is proposed.Firstly,the threedimensional space is divided into grids,so that the algorithm can be applied to three-dimensional flight path planning.Then,a non-uniform initial pheromone matrix is established,and an adaptive pheromone volatile factor is added,which can improve the searching efficiency and speed up the convergence rate of the algorithm.Finally,the objective function of UAV flight path optimization is further established by defining the 3D length index function and the 3D angle index function,and the global optimization of 3D flight path planning is realized.The simulation results show that the proposed algorithm has shorter running time and faster convergence speed,and the planned flight path is also shorter and smoother.

关 键 词：无人机 航迹规划 蚁群算法 自适应算法 

分 类 号：V279[航空宇航科学与技术—飞行器设计]