无人机路径规划:一种粒子群和灰狼复合算法  

Path Planning of the Unmanned Aerial Vehicle:a Compound Algorithm of Particle Swarm Optimization and Grey Wolf Optimization

作　　者：智瀚宇 贾新春[1] 张学立 ZHI Hanyu;JIA Xinchun;ZHANG Xueli(School of Automation and Software Engineering,Shanxi University,Taiyuan 030031,China)

机构地区：[1]山西大学自动化与软件学院,山西太原030031

出　　处：《控制工程》2025年第4期720-727,共8页Control Engineering of China

基　　金：国家自然科学基金资助项目(U1610116,61973201)。

摘　　要：针对无人机在复杂环境下的三维路径规划问题,集成传统的粒子群优化(particle swarm optimization,PSO)算法和灰狼优化(grey wolf optimization,GWO)算法,提出了一种PSO-GWO复合算法。首先,采用了非线性控制参数和加权自适应的个体位置更新策略来平衡算法的全局搜索能力和局部搜索能力;然后,使用随机指导策略来增加解的多样性;最后,使用B样条曲线平滑所生成的飞行路径,使路径更适用于无人机。实验结果表明,PSO-GWO复合算法可以生成一条安全可行的路径,其性能明显优于GWO算法和其他改进GWO算法。To solve the problem of three-dimensional path planning of the unmanned aerial vehicle(UAV)in complex environment,a compound algorithm is proposed by integrating the traditional particle swarm optimization(PSO)algorithm and the grey wolf optimization(GWO)algorithm,called the PSO-GWO compound algorithm.Firstly,nonlinear control parameters and weighted adaptive individual location update strategy are used to balance the global search capability and local search capability of the algorithm.Secondly,the random guidance strategy is used to increase the diversity of the solutions.Finally,B-spline curve is used to smooth the generated flight path to make the path more suitable for the UAV.The experimental results show that the PSO-GWO compound algorithm can generate a safe and feasible path,and its performance is significantly better than that of the GWO and other improved GWO algorithms.

关 键 词：三维路径规划 粒子群优化算法 灰狼优化算法 B样条曲线 

分 类 号：TP242[自动化与计算机技术—检测技术与自动化装置]