集群无人机激光充电策略  

aser charging strategy of the cluster UAV

作　　者：李大林 仲元昌[1] 陈宇[1] 杨子楚 LI Da-lin;ZHONG Yuan-chang;CHEN Yu;YANG Zi-chu(School of Electrical Engineering,Chongqing University,Chongqing 400044,China)

机构地区：[1]重庆大学电气工程及其自动化学院,重庆400044

出　　处：《激光与红外》2024年第8期1221-1228,共8页Laser & Infrared

基　　金：国家自然科学基金项目(No.U22B2095);国网重庆市电力公司电力科学研究院项目(No.H20221585)资助。

摘　　要：如今无人机(UAV)越来越趋向于群体运用,无人机群中的UAV数量也越来越多,但UAV的续航一直是限制其应用的原因。为了解决无人机群续航较短,充电效率低等问题,本文针对激光供电无人机群,提出一种同时充电的集群策略。利用圆中圆公式推导生成集群充电UAV的初始位置,后利用改进人工势场(IAPF)结合粒子群优化(PSO)算法对其位置进行优化,最后通过MATLAB仿真验证集群结果。结果表明激光光束为1束和7束情况下,集群最高效率为92.6%和91.9%,效率比优化前平均提升12.64%和10.41%,光斑外径下降7.06%和6.27%,有较好的集群效果。该集群充电策略能够解决传统充电方法在多数量无人机群时的调度问题,可以用在无人机群充电领域。Nowadays,Unmanned Aerial Vehicles(UAV)is increasingly being used in groups,and the number of UAV in cluster is increasing,yet the endurance of UAVs has been the reason that limits its application.In this paper,a cluster strategy of simultaneous charging for laser-powered UAV cluster is proposed to solve the problems of short endurance and low charging efficiency of UAV cluster.The initial position of the cluster charging UAVs are derived through the formula of circle flocking,then optimized by using the Improved Artificial Potential Field(IAPF)combining with the Particle Swarm Optimization(PSO)algorithm and finally the results are verified by MATLAB simulation at last.The results show that in the case of laser beams of 1 and 7 beams,the highest efficiency of clustering is 92.6%and 91.9%,with an average increase of 12.64%and 10.41%in efficiency over the pre-optimization period,and a decrease of 7.06%and 6.27%in spot OD,which gives a better effect of clustering.The cluster charging strategy can address the scheduling problem of the traditional charging method when there are many numbers of UAV swarms,which can be used in the field of UAVs laser charging.

关 键 词：激光充电 无人机群 改进人工势场法 粒子群优化算法 

分 类 号：V279[航空宇航科学与技术—飞行器设计] TN249[电子电信—物理电子学]