基于改进粒子群算法的无人机三维航迹规划  

Three-dimensional Trajectory Planning of UAV Based on Improved Particle Swarm Optimization Algorithm

作　　者：陈明强[1] 李奇峰 冯树娟 徐开俊[1] CHEN Mingqiang;LI Qifeng;FENG Shujuan;XU Kaijun(School of Flight Technology,Civil Aviation Flight University of China,Guanghan 618307,China)

机构地区：[1]中国民用航空飞行学院飞行技术学院,四川广汉618307

出　　处：《无线电工程》2023年第2期394-400,共7页Radio Engineering

基　　金：民航飞行技术与飞行安全重点实验室自主研究项目(FZ2021ZZ06);高质量民航特色“交通运输”硕士专业学位平台体系建设(MHJY2022001);大学生创新创业训练计划项目(S202210624187)。

摘　　要：针对传统粒子群(Particle Swarm Optimization, PSO)算法在航迹规划的过程中需要根据无人机性能建立约束条件和易陷入局部最优值的缺点,提出了一种结合天牛须(Beetle Antennae Search, BAS)算法的球坐标PSO算法。该改进算法直接利用球坐标系对无人机的航向角和俯仰角进行约束,并且通过BAS算法避免PSO算法陷入局部最优值。根据数字高程地图建立仿真环境,综合考虑航迹长度、平滑度和危险性等因素构建目标函数。仿真结果表明,改进后的算法与其他PSO算法相比,规划的三维航迹质量更高,能够很好地适应无人机在各种环境下的飞行要求。To address the shortcomings of the traditional Particle Swarm Optimization(PSO) algorithm in the process of trajectory planning, which requires the establishment of constraints according to the UAV performance and tends to fall into the local optimum, a PSO algorithm in spherical coordinates combined with the Beetle Antennae Search(BAS) algorithm is proposed. The improved algorithm directly uses the spherical coordinate system to constrain the UAV heading and pitch angles and avoids the PSO falling into the local optimum by the BAS. The simulation environment is established based on the digital elevation map, and the objective function is constructed based on the length of trajectory, smoothness and danger. The simulation results show that the improved algorithm can plan a higher quality 3D trajectory compared with other PSO algorithms, and can be well adapted to the flight requirements of UAVs in various environments.

关 键 词：无人机 三维航迹规划 粒子群算法 天牛须算法 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程] V249[自动化与计算机技术—控制科学与工程]