基于改进自适应蚁群算法的无人机航迹规划研究  

Path Planning of UAV Based on Improved Adaptive Ant Colony Algorithm

作　　者：陈侠[1] 毛海亮 刘奎武 CHEN Xia;MAO Hailiang;LIU Kuiwu(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9期6-10,共5页Electronics Optics & Control

基　　金：国家自然科学基金(61074159,61906125)。

摘　　要：针对传统蚁群(ACO)算法在无人机航迹规划中存在的收敛速度慢、易陷入局部最优等缺点,提出了一种基于改进自适应蚁群(IAACO)算法的无人机航迹规划方法。首先,将角度导向因子引入状态转移规则中,使蚂蚁以更大的概率朝着目标点的方向前进,提高了路径的搜索效率;然后,引入启发式信息自适应调整因子平衡了算法的收敛性和全局搜索能力;最后,通过定义长度指标函数、角度指标函数,进一步建立了航迹优化的目标函数,实现了无人机航迹规划的全局优化。实验结果表明,改进后的算法收敛速度更快,生成的路径更平滑、长度更短。In view of the shortcomings of traditional Ant Colony Optimization(ACO) algorithm in path planning of Unmanned Aerial Vehicle(UAV),such as slow convergence speed and easy to fall into local optimal solution,an Improved Adaptive Ant Colony Optimization(IAACO) algorithm is proposed.Firstly,in order to make ants move in the direction of the target point with greater probability and improve the search efficiency of the path,an angle guidance factor is introduced into the transfer probability of ACO.Then,heuristic information adaptive adjustment factor is introduced to balance the convergence and global search ability of the algorithm.Finally,by defining length index function and angle index function,the objective function of route optimization is further established,and the global optimization of UAV route planning is realized.Experimental results show that the improved algorithm converges faster,and the generated track is smoother and shorter.

关 键 词：无人机 航迹规划 自适应蚁群算法 搜索效率 

分 类 号：V279[航空宇航科学与技术—飞行器设计]