改进粒子群算法的UAV突防路径规划  

Path Planning of UAV Penetration Based on Improved Particle Swarm Optimization

作　　者：赵棣宇 郑宾 殷云华 郭华玲 陈霏 冯广义 ZHAO Diyu;ZHENG Bin;YIN Yunhua;GUO Hualing;CHEN Fei;FENG Guangyi(School of Electronics and Control Engineering,North University of China,Taiyuan 030000,China;Science and Technology on Transient Impact Laboratory,Beijing 102000,China)

机构地区：[1]中北大学电气与控制工程学院,太原030000 [2]瞬态冲击技术重点实验室,北京102000

出　　处：《电光与控制》2023年第4期12-16,39,共6页Electronics Optics & Control

基　　金：瞬态冲击技术重点实验室基金(6142606203208);山西省基础研究计划(自由探索类)(202103021224221)。

摘　　要：面对复杂地形条件下的无人机突防任务,粒子群算法(PSO)在寻找最优路径的过程中易陷入局部最优、搜索时间过长等困境。针对上述问题,在PSO中引入球坐标系,将所得的路径看作向量。通过向量的距离、仰角和方位角与无人机的速度、俯仰角和转向角的相互关系来实现粒子的迭代更新。最后,引入随机自适应惯性权重,弥补粒子前期局部搜索能力与后期全局搜索能力的不足。仿真结果表明,改进算法能够有效规避威胁区域,收敛速度更快,收敛精度更高,且不易陷入局部最优。In the face of UAV penetration tasks under complex terrain conditions,Particle Swarm Optimization(PSO)is easy to fall into local optimum and suffers from long search time in the process of finding the optimal path.To solve the above problems,the spherical coordinate system is introduced into PSO,and the path obtained is regarded as a vector.The iterative updating of particles is realized through the relationship between the distance,elevation and azimuth of the vector and the speed,pitch and steering angle of the UAV.Finally,the random adaptive inertia weight is introduced to make up for the deficiency of the particles local search ability in the early stage and global search ability in the later stage.The simulation results show that the improved algorithm can effectively avoid the threat region,has faster convergence rate and higher convergence accuracy,and is not easy to fall into local optimum.

关 键 词：无人机 低空突防 粒子群算法 球坐标 自适应惯性权重 

分 类 号：V249[航空宇航科学与技术—飞行器设计]