基于APF-RRT算法的无人机航迹规划  

UAV Path Planning Based on APF-RRT Algorithm

作　　者：陈侠[1] 刘奎武 毛海亮 CHEN Xia;LIU Kuiwu;MAO Hailiang(Shenyang Aerospace University,Shenyang 110000,China)

机构地区：[1]沈阳航空航天大学,沈阳110000

出　　处：《电光与控制》2022年第5期17-22,共6页Electronics Optics & Control

基　　金：国家自然科学基金(61074159,61906125)。

摘　　要：提出了一种基于改进快速随机搜索树算法、人工势场法和遗传算法相融合(APF-RRT)的无人机航迹规划方法,以解决搜索范围随机性强和收敛速度较慢的问题。首先,引入目标偏置来引导随机采样点的生成,因目标点有一定的概率成为采样点,从而减少采样数量。同时,加入改进人工势场法来改进新节点的生成方向,将目标点与障碍物的合力方向作为搜索树的生长方向,提高了路径搜索的效率。然后,将改进后的RRT算法生成的一组航迹点作为遗传算法的初始种群,并建立了适应度函数模型,利用遗传算法对路径进行了优化,获得较优路径,解决了路径随机性问题。最后,仿真结果表明改进后算法生成的路径长度更短,运行时间更短。An Unmanned Aerial Vehicle(UAV) path planning method based on improved Rapidly-exploring Random Tree(RRT) algorithm combined with Artificial Potential Field(APF) and genetic algorithm is proposed to overcome the disadvantages of strong randomness of search range and slow convergence rate.Firstly,the target bias is introduced to guide the generation of random sampling points,and the target points have a certain probability of becoming sampling points,thus reducing the number of samples.Meanwhile,the APF is introduced to improve the generation direction of the new node,and the direction of the resultant force between the target point and obstacles is taken as the growth direction of the search tree,which improves the efficiency of path search.Then,a group of track points generated by the improved RRT algorithm are used as the initial population of the genetic algorithm,and a fitness function model is established.The genetic algorithm is applied to optimize the path,and the optimal path is obtained,which solves the problem of path randomness.Finally,the simulation results show that the path generated by the improved algorithm is shorter in length and consumes less time.

关 键 词：无人机 航迹规划 快速随机搜索树 目标偏置 人工势场法 遗传算法 

分 类 号：V279[航空宇航科学与技术—飞行器设计]