基于改进哈里斯鹰算法的无人机三维路径规划  

Unmanned Aerial Vehicle 3D Path Planning Based on Improved Harris's Hawk Algorithm

作　　者：李杰[1] 陶伟 张娟 李辉[1] LI Jie;TAO Wei;ZHANG Juan;LI Hui(School of Computer Science,Sichuan University,Chengdu 610065,China)

机构地区：[1]四川大学计算机学院,成都610065

出　　处：《火力与指挥控制》2024年第12期27-35,46,共10页Fire Control & Command Control

基　　金：国家自然科学基金重点资助项目(U20A20161)。

摘　　要：在无人机系统中,为无人机快速规划一条低成本、安全且满足机动条件的飞行路径是一个重要问题。为了满足低成本、安全、快速的路径规划要求,综合考虑路径长度、障碍物约束和航迹平滑度,构建代价函数。平滑度指标反映无人机的性能约束,简化性能考量。在此基础上,提出一种改进哈里斯鹰算法,求解无人机三维路径规划问题。该改进方法利用球面矢量编码,直接实现转弯和爬升角的约束,显著减小搜索空间。利用高斯突变策略,跨越障碍物产生的极值点,提高算法的全局搜索能力。引入差分进化策略,促进个体间信息交换,提升算法的局部寻优能力。仿真结果表明,该算法与其他群智能算法相比,具有更快的收敛速度,有更好的路径规划效果。In UAV systems,swifly planning a low-cost,safe,and maneuver-compliant flight path for drones is pivotal.To meet the requirements of cost-effectiveness,safety,and rapid planning,a cost function is devised,considering path length,obstacle constraints,and trajectory smoothness.Smoothness metrics reflect drone performance constraints,simplifying performance considerations.Building upon this,an enhanced Harris Hawk algorithm is proposed to address three-dimensional drone path planning.This enhanced approach employs spherical vector encoding to directly enforce constraints on turns and climb angles,significantly reducing the search space.Utilizing Gaussian mutation strategy to traverse extreme points generated by obstacles enhances the algorithm's global search capability.Introducing dif-ferential evolution strategy fosters information exchange among individuals,boosting the algorithm's local optimization ability.Simulation results demonstrate that compared to other swarm intelligence algorithms,this algorithm exhibits faster convergence and superior path planning effectiveness.

关 键 词：无人机路径规划 哈里斯鹰算法 球面矢量编码 高斯突变策略 差分进化算法 

分 类 号：V249[航空宇航科学与技术—飞行器设计]