联合多核FCM和改进GOA的多无人机协同侦查航迹规划  

Multi UAV Cooperative Reconnaissance Path Planning Algorithm Based on Multi-Core FCM and Improved Locust Optimization Algorithm

作　　者：靳江锋 刘旭[1] JIN Jiangfeng;LIU Xu(Chinese Flight Test Establishment, Xi’an 710089, China)

机构地区：[1]中国飞行试验研究院,西安710089

出　　处：《兵器装备工程学报》2021年第11期181-188,共8页Journal of Ordnance Equipment Engineering

基　　金：装发共用技术(41411030301)。

摘　　要：针对多无人机协同侦查效率较低,提出了一种新的多无人机协同侦察航迹规划算法。构建了基于任务需求导向的协同侦查航迹规划模型,采用改进的多核FCM对多类型侦查目标进行聚类分析,在实现目标自适应高质量聚类分析的同时,改变了传统“以我为主”的侦查作战样式。建立了时间代价最优航迹规划目标函数,并引入改进的蝗虫优化算法进行求解,通过重新定义蝗虫编码和迭代更新方式,得到多无人机协同侦查航迹,从而完成对目标的全覆盖、差异化侦查。仿真结果表明:提出的航迹规划算法更贴近实际应用,时间代价降低约8.9%~10.7%。Aiming at the low efficiency of multi UAV cooperative reconnaissance,a new path planning algorithm for multi UAV cooperative reconnaissance was proposed.The path planning model of cooperative reconnaissance was constructed based on task demand orientation,and the improved multi-core FCM was applied to cluster analysis of multiple types of reconnaissance targets,which not only realizes the self-adaptive high-quality clustering analysis of targets,but also changes the traditional“self-centered”investigative combat style.On this basis,the target function of time cost optimal path planning was established,and the improved locust optimization algorithm was introduced to obtain the solution.By redefining the locust code and iterative update mode,the cooperative reconnaissance path of multiple UAV was finally obtained,so as to complete the full coverage and differential reconnaissance of targets.The simulation results show that the proposed path planning algorithm is closer to the practical application,and the time cost is reduced by about 8.9%~10.7%.

关 键 词：多无人机 航迹规划 协同 蝗虫优化算法 时间代价 

分 类 号：TJV279[兵器科学与技术]