摘要
为解决不确定环境中多无人机路径搜索针对性不强、效率低问题,提出一种基于粒子群遗传算法的多无人机协同路径搜索方法。建立区域栅格图环境和搜索概率图模型,采取滚动预测的方式,提出使用协同粒子群遗传算法生成预测路径,通过适应度函数确定最优搜索路径,该路径满足无人机最小转弯半径限制,并能实现威胁区域规避和重点区域加强搜索。仿真结果验证了所提算法的有效性。
In order to solve such problems as weak pertinence and low efficiency of multi-UAV path search in uncertain environment,a multi-UAVs collaborative path search method based on particle swarm genetic algorithm(GA)is proposed.First,a regional raster map environment and a search probability map model are established,then a rolling prediction method is adopted,and a collaborative particle swarm genetic algorithm is used to generate a prediction path.Finally,the optimal prediction search path is determined by the adaptability function.The path can meet with the minimum turning radius constraint of the UAV,and can realize the avoidance of threat areas and can strengthen the search for key areas.The simulation results verifies the effectiveness of the proposed algorithm.
作者
姚军
李思捷
罗德林
刘善国
王国耀
吴军
YAO Jun;LI Si-jie;LUO De-lin;LIU Shan-guo;WANG Guo-yao;WU Jun(Unit 95894 of PLA,Beijing 102211,China;Xiamen University,Xiamen 361102,China)
出处
《火力与指挥控制》
CSCD
北大核心
2021年第8期59-63,70,共6页
Fire Control & Command Control
基金
航空电子科技实验室、航空科学基金联合资助项目(20185568005)。
关键词
多无人机
协同搜索
协同粒子群遗传算法
路径规划
multiple unmanned aerial vehicle
cooperative search
collaborative PSO and GA
path planning
