改进蚁群算法无人机三维航迹规划  

Improved Ant Colony Algorithm for 3D Trajectory Planning of Unmanned Aerial Vehicles

作　　者：时光泰 王合龙[1] SHI Guangtai;WANG Helong(Luoyang Institute of Electro-Optical Equipment AVIC,Luoyang 471000 China)

机构地区：[1]中国航空工业集团公司洛阳电光设备研究所,河南洛阳471000

出　　处：《电光与控制》2025年第2期18-23,共6页Electronics Optics & Control

摘　　要：蚁群算法经常被用于解决无人机航迹规划问题,而传统蚁群算法存在迭代速度慢、易陷入局部最优等诸多缺陷,针对这些问题提出了一系列改进措施:对于航迹规划初期的蚁群算法盲目搜索问题,在任务空间中对信息素进行具有引导性的不均匀分配,使得蚂蚁沿着起点到终点的连线进行探索,蚁群的探索更具方向性;同时,在启发函数中考虑到了转角因素对航迹平滑性的影响,用以提升航迹规划的质量;另外,采用自适应挥发系数,动态调整信息素挥发速率,避免前期过于快速地收敛到局部最优,也确保后期加速收敛,不使算法陷入无休止的计算中,并采用冗余节点消除策略对航迹做了进一步优化。Ant colony algorithm is often used to solve the problem of UAV path planning but the traditional ant colony algorithm has many defects such as slow iteration speed and easy to fall into local optimization.A series of improvement measures are proposed to address these issues.For the blind search problem of ant colony algorithm in the early stage of route planning pheromones are distributed unevenly and guidingly in the task space which makes ants explore along the line from the starting point to the target point and the exploration of ant colony is more directional.At the same time the influence of rotation angle on track smoothness is considered in the heuristic function to improve the quality of track planning.In addition the adaptive volatilization coefficient is used to dynamically adjust the pheromone volatilization rate so as to avoid premature convergence to the local optimum and to ensure the accelerated convergence in the later period thus to prevent the algorithm from falling into endless calculation and a redundant node elimination strategy is used to further optimize the track.

关 键 词：无人机航迹规划 蚁群算法 自适应挥发系数 信息素差异分布策略 

分 类 号：V249[航空宇航科学与技术—飞行器设计]