摘要
为了克服山区完成物流配送任务时无人机在三维飞行路径规划中采用蚁群算法(ACO)易陷入局部极值、前期收敛速度慢的难题,提出了一种改进ACO的无人机三维航迹规划优化算法。建立了适合无人机飞行的三维环境模型;综合考虑航迹长度、障碍物碰撞和高度变化等因素,构建了适应度函数;提出了一种改进信息素更新规则的蚁群算法,在启发函数中加入航迹引导因子,以克服ACO算法前期搜索效率低和收敛速度慢的问题,避免陷入局部极值。以220 km×220 km×80 km任务空间的三维航迹规划为例,仿真实验数据显示:采用改进后的算法可避免ACO算法的缺陷,验证了所规划出的航迹最短且其综合指标最佳。研究结果表明:采用新航迹规划算法在山区三维环境下进行物流配送是可行和有效的。
In order to overcome the problems that Ant Colony Optimization(ACO) is easy to fall into local extreme values or a low convergence speed will be measured in the early stage when a UAV is used in 3D flight trajectory planning during logistics distribution tasks in mountainous areas,this paper proposes an optimization algorithm for 3D flight trajectory planning of a UAV based on improved ACO.A 3D environment model suitable for UAV flight is established.The fitness function is constructed based on the comprehensive consideration of trajectory length,obstacle collision and height change.An ACO is proposed to improve pheromone updating rules,which adds a trajectory guiding factor into the heuristic function to overcome the low search efficiency and a low convergence speed of ACO algorithm in the early stage,and avoid falling into local extreme values.In this paper,3D trajectory planning of 220 km×220 km×80 km mission space is taken as an example.The simulation results show that the improved algorithm can avoid the defects of ACO algorithm,and verify that the planned trajectory is the shortest and its comprehensive index is the best.The results show that the proposed trajectory planning algorithm is feasible and effective for a UAV to carry out logistics distribution in 3D mountainous environment.
作者
巫茜
黄浩
曾青
王成睿
邝茜
WU Qian;HUANG Hao;ZENG Qing;WANG Chengrui;KUANG Xi(College of Computer Science and Engineering,Chongqing University of Technology,Chongqing 400054,China)
出处
《重庆理工大学学报（自然科学）》
CAS
北大核心
2022年第10期185-191,共7页
Journal of Chongqing University of Technology：Natural Science
基金
重庆市科技局重点项目(cstc2019jscx-fxydX0047,cstc2019jscx-fxydX0090)。
关键词
航迹规划
蚁群算法
航迹引导因子
信息素更新规则
trajectory planning
Ant Colony Optimization
trajectory guiding factor
pheromone updating rule
