基于离散粒子群算法的集群无人机飞行路径规划  

Clustering UAV flight path planning based on DPSO

作　　者：广鑫 耿增显[1] GUANG Xin;GENG Zengxian(Civil Aviation University of China,Tianjin 300300,China)

机构地区：[1]中国民航大学,天津300300

出　　处：《现代电子技术》2025年第4期119-122,共4页Modern Electronics Technique

基　　金：国家外国专家项目(DL2022202002L);国家重点研发计划(2022YFB4300904)。

摘　　要：飞行环境可能随时发生变化,如新的障碍物出现、天气条件变化等,导致集群无人机飞行路径规划难度上升。为此,提出一种基于离散粒子群算法的集群无人机飞行路径规划方法。根据人工势场理论与威胁类型绘制Voronoi图,从而确定Voronoi图弧权值。结合Voronoi图弧权值计算结果与无人机飞行航程、威胁、电池效能代价构建适应度函数,通过离散粒子群算法不断进行迭代寻优,得到集群无人机的最佳飞行路径。实验结果表明,所提方法在集群无人机路径规划中具有较高的执行效率和成功率,具有良好的实际应用前景。The flight environment may change at any time,such as the appearance of new obstacles,changes in weather conditions,etc.,which increases the difficulty of clustering unmanned aerial vehicle(UVA)flight path planning.Therefore,a method of clustering UVA flight path planning based on discrete particle swarm optimization algorithm(DPSO)is proposed.A Voronoi diagram is drew based on artificial potential field theory and threat types to determine the arc weights of the Voronoi diagram.By combining the calculation results of Voronoi plot arc weight with the UVA flight range,threat,and battery efficiency cost,a fitness function is constructed.The continuous iterative optimization is carried out by means of the theory of DPSO,so as to obtain the optimal flight path for the clustering UVA.The experimental results show that the proposed method has high execution efficiency and success rate in clustering UVA path planning,and has good practical application prospect.

关 键 词：离散粒子群算法 集群无人机 路径规划 人工势场 VORONOI图 适应度函数 

分 类 号：TN96-34[电子电信—信号与信息处理] TP391.4[电子电信—信息与通信工程]