基于改进蝗虫优化算法考虑任务威胁的多无人机协同航迹规划  

Collaborative Route Planning of Multiple Unmanned Aerial Vehicles Considering Task Threats Based on Improved Grasshopper Optimization Algorithm

作　　者：郭志明 娄文忠[1] 李涛[2] 张梦宇[2] 白子龙 乔虎 GUO Zhiming;LOU Wenzhong;LI Tao;ZHANG Mengyu;BAI Zilong;QIAO Hu(School of Mechatronical Engineering,Beijing Institute of Technology,Beijing 100081,China;China Research and Development Academy of Machinery Equipment,Beijing 100089,China;School of Mechatronic Engineering,Xi'an Technological University,Xi'an 710021,Shaanxi,China)

机构地区：[1]北京理工大学机电学院,北京100081 [2]中国兵器科学研究院,北京100089 [3]西安工业大学机电工程学院,陕西西安710021

出　　处：《兵工学报》2023年第S02期52-60,共9页Acta Armamentarii

摘　　要：为使多无人机(UAV)在面临不同程度的任务威胁环境时能够高效的执行任务,研究并设计一种新型协同航迹规划算法,以综合代价为目标函数,利用改进的蝗虫优化算法对构建的航迹规划模型进行求解。分析传统蝗虫算法的原理以及不足,提出改进策略,即引入基于逻辑斯蒂函数的非线性递减策略;针对改进之后的算法进行仿真测试,并与其他算法进行对比,验证算法的应用效果。仿真结果显示,相对于其他算法,改进算法具有明显的优势,收敛速度更高,航迹代价更低,可为UAV作战效能提升提供支撑。To enable multiple unmanned aerial vehicles(UAVs)to efficiently execute tasks when facing varying degrees of mission threat environments,a collaborative route planning algorithm of UAVs based on improved grasshopper optimization algorithm is proposed.A route planning model is established by taking the comprehensive cost as an objective function.The grasshopper optimization algorithm is improved by introducing a nonlinear descent strategy based on the logistic function.The feasibility of the improved grasshopper optimization algorithm is verified through simulation experiment.The experimental results showed that the improved grasshopper optimization algorithm has faster convergence speed and global search ability,which can provide support for improving the combat effectiveness of unmanned aerial vehicles.

关 键 词：多无人机协同 航迹规划 改进蝗虫算法 任务威胁 

分 类 号：V249[航空宇航科学与技术—飞行器设计]