华南理工大学学报(自然科学版) ›› 2011, Vol. 39 ›› Issue (10): 37-43.doi: 10.3969/j.issn.1000-565X.2011.10.007

• 电子、通信与自动控制 • 上一篇    下一篇

采用多重启发蚁群优化算法的无人机航迹规划

李猛 王道波 盛守照   

  1. 南京航空航天大学 自动化学院,江苏 南京 210016
  • 收稿日期:2011-04-19 修回日期:2011-07-18 出版日期:2011-10-25 发布日期:2011-09-01
  • 通信作者: 李猛(1982-) ,男,博士生,主要从事无人机飞行控制与任务规划研究. E-mail:limengabcd@126.com
  • 作者简介:李猛(1982-) ,男,博士生,主要从事无人机飞行控制与任务规划研究.
  • 基金资助:

    航空科学基金资助项目( 20101352015)

UAV Route Planning Using Multi-Heuristic Ant Colony Optimization Algorithm

Li Meng  Wang Dao-bo  Sheng Shou-zhao   

  1. College of Automation Engineering,Nanjing University of Aeronautics and Astronautics,Nanjing 210016,Jiangsu,China
  • Received:2011-04-19 Revised:2011-07-18 Online:2011-10-25 Published:2011-09-01
  • Contact: 李猛(1982-) ,男,博士生,主要从事无人机飞行控制与任务规划研究. E-mail:limengabcd@126.com
  • About author:李猛(1982-) ,男,博士生,主要从事无人机飞行控制与任务规划研究.
  • Supported by:

    航空科学基金资助项目( 20101352015)

摘要: 为解决复杂环境下的无人机航迹规划问题,提出了一种多重启发蚁群优化算法.该算法综合考虑无人机当前位置与待选位置之间的距离和威胁分布,以及待选位置与目标位置之间的距离和威胁分布,将这些已知信息构造为蚂蚁状态转移的多重启发信息,指导蚂蚁的搜索行为.文中对多重启发蚁群优化算法的收敛性进行了分析,并针对航迹不可行和任务区域内存在的突发威胁,分别给出了航迹平滑方法和在线航迹再规划方法.仿真结果表明: 所提方法能够有效地增强蚁群优化算法的航迹规划能力,提高收敛的速度和精度,得到最优的飞行航迹.

关键词: 航迹规划, 多重启发, 蚁群优化算法, 无人机

Abstract:

In this paper,a multi-heuristic ant colony optimization algorithm is proposed for the route planning of the unmanned aerial vehicle ( UAV) in complex environments. In the algorithm,the distance and the threat distribution between the current UAV position and the candidate one,as well as between the candidate position and the target one,are designed as the multi-heuristic information in the state transition of ants to guide their search behaviors. Moreover,the convergence of the ant colony optimization algorithm is analyzed,and the route smoothing and online route replanning methods are presented respectively for the unfeasible route and the pop-up threats in the task region. Simulation results show that the proposed methods can effectively enhance the route planning ability of the ant colony optimization algorithm and improve the speed and precision of the convergence,thus achieving the optimal route.

Key words: route planning, multi-heuristic, ant colony optimization algorithm, unmanned aerial vehicles

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