基于CLGWO的无人机三维路径规划研究  

Research on three-dimensional path planning of unmanned aerial vehicles based on CLGWO

作　　者：陈福金 CHEN Fujin(Zhuhai Surveying and Mapping Academy,Zhuhai 519000,China)

机构地区：[1]珠海市测绘院,广东珠海519000

出　　处：《经纬天地》2024年第3期77-82,共6页Survey World

摘　　要：目前,无人机三维路径规划能力欠缺,常用的灰狼优化算法存在搜索速度较慢、易陷入局部最优解的问题。鉴于此,研究引入反向学习策略改进灰狼优化算法,旨在提升无人机三维路径规划能力。结果表明:研究方法在多峰函数F_(1)到F_(4)的性能测试中,分别在迭代次数200、100、200、50达到最优值。在简单的环境中,研究方法展示的性能最优,最优路径长度992 m,平均迭代次数281,平均运行时间8.11 s;在复杂的环境中,最优路径长度1003 m,平均迭代次数312,平均运行时间12.37 s。由此可见,研究所改进的三维路径规划算法具有较好的路径寻优能力,将该方法应用于无人机航拍领域中能够更快更好地搜索最优路径,从而顺利完成各类搜索任务。At present,the three-dimensional path planning ability of unmanned aerial vehicles is lacking,and commonly used Grey Wolf Optimization Algorithms have slow search speed and are prone to falling into local optimal solutions.In view of this,the reverse learning strategies are introduced in the study to improve the Grey Wolf Optimization Algorithm,so as to enhance the threedimensional path planning ability of unmanned aerial vehicles.The results indicate that the research method achieved optimal values in the performance testing of multi-modal functions F_(1) to F_(4) at iterations of 200,100,200,and 50 respectively.In a simple environment,the research method exhibits the best performance,with an optimal path length of 992 m,an average number of iterations of 281,and an average running time of 8.11 s,in a complex environment,the optimal path length is 1003 m,the average number of iterations is 312,and an average running time is 12.37 s.It can be seen that the improved 3D path planning algorithm has a good path optimization ability,and the application of this method in the field of UAV aerial photography can search the optimal path faster and better,so as to successfully complete various search tasks.

关 键 词：无人机 三维路径规划 灰狼优化算法 反向学习策略 路径寻优 

分 类 号：P231[天文地球—摄影测量与遥感]