摘要
为解决固定翼无人机集群的快速任务规划问题,提出一种基于聚类算法和改进化学反应优化模型的快速集群任务规划算法,满足固定翼无人机集群对规划时间的限制和实际飞行中机载计算机工作场景。利用聚类的方式降低算法的计算负担,通过改进化学反应优化算法保证规划问题的可靠快速收敛,实现无人机集群的快速任务规划。数值仿真结果表明。在与同类算法的比较中该算法有更好的计算效率和收敛效果,通过硬件在环仿真实验模拟了算法在机载计算机环境下运行的效率和结果,验证了在真实场景下算法的可靠性。
To solve the fast task planning problem of fixed wing UAV swarm,a fast swarm task planning algorithm based on clustering algorithm and improved chemical reaction optimization model was proposed to meet the limitation of fixed wing UAV swarm on planning time and the working scene of airborne computer in actual flight.Clustering was used to reduce the computational burden of the algorithm,and the chemical reaction optimization algorithm was improved to ensure the reliable and rapid convergence of the planning problem,and the rapid task planning of UAV swarm was realized.Numerical simulation shows that the algorithm has better computational efficiency and convergence effect compared with similar algorithms.Through hardware in the loop simulation experiment,the efficiency and results of the algorithm in the airborne computer environment are simulated,and the reliability of the algorithm in the real scene is verified.
作者
吴昌伟
谢红薇
UAV swarms WU Chang-wei;XIE Hong-wei(College of Software,Taiyuan University of Technology,Taiyuan 030600,China)
出处
《计算机工程与设计》
北大核心
2023年第10期2979-2987,共9页
Computer Engineering and Design
基金
国家自然科学基金项目(61872262)。
关键词
固定翼无人机集群
快速集群任务规划
聚类算法
化学反应优化
机载计算机
嵌入式环境
硬件在环仿真
fixed-wing UAV swarm
fast swarm mission planning
clustering algorithm
chemical reaction optimization
companion computer
embedded environment
hardware-in-the-loop simulation
