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应用科学学报 ›› 2012, Vol. 30 ›› Issue (2): 215-220.doi: 10.3969/j.issn.0255-8297.2012.02.017

• 论文 • 上一篇    下一篇

基于蚁群优化算法和人工势场的无人机航迹规划

李猛， 王道波， 柏婷婷， 盛守照   

  1. 南京航空航天大学自动化学院，南京210016
  • 收稿日期:2011-05-12 修回日期:2011-08-17 出版日期:2012-03-26 发布日期:2012-03-30
  • 通信作者: 李猛，博士生，研究方向：无人机飞行控制与任务规划，E-mail：limengabcd@126.com；
  • 作者简介:李猛，博士生，研究方向：无人机飞行控制与任务规划，E-mail：limengabcd@126.com；王道波，教授，博导，研究方向：无人机飞行控制、精密伺服控制等，E-mail：dbwangpe@nuaa.edu.cn
  • 基金资助:

    航空科学基金(No.20101352015)；南京航空航天大学基本科研业务费研究基金(No.V1073-031，No.NP2011049)资助

UAV Route Planning Based on Ant Colony Optimization and Artificial Potential

LI Meng, WANG Dao-bo, BAI Ting-ting, SHENG Shou-zhao   

  1. College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China  
  • Received:2011-05-12 Revised:2011-08-17 Online:2012-03-26 Published:2012-03-30

PDF

1548

被引次数

19

摘要：

针对复杂环境下的无人机航迹规划问题，建立栅格化环境模型，提出了结合蚁群算法与人工势场的航迹规划方法. 在航迹搜索过程中，蚂蚁不仅受到信息素和启发信息作用，还受到势场力的影响. 根据节点位置的势场力分布，提出了确定性选择和概率性选择相结合的状态转移规则，并设计环境感知因子，动态调整确定性选择的比例. 将节点的势场方向、节点与目标间的距离构造蚂蚁的综合启发信息，以充分利用对已知环境的认知，指引蚂蚁搜索. 仿真结果表明所提方法能有效得到无人机的最优航迹，优化效果优于单一的蚁群算法和人工势场法，具有更好的收敛速度和优化精度.

关键词: 航迹规划, 蚁群优化算法, 人工势场, 无人机

Abstract:

To deal with dynamic routes planning of unmanned aerial vehicles in a complicated environment, a new method that combines ant colony optimization with artificial potential is proposed. The mission region is described as a grid model. In the route search process, ants are influenced not only by pheromone and heuristic information, but also by the potential field force. According to the node location’s potential field, the state transition rules consist of deterministic choice and probabilistic choice. The environmental perception factor is designed for dynamically adjusting the proportion of deterministic choice. In order to make full use of the known environmental information and guide the ant’s search, the potential field direction and the distance between the candidate node and the goal are used to construct comprehensive heuristic information. Simulation results show that the proposed method can effectively obtain optimal feasible routes. The optimization result is better than that of the simplex ant colony and artificial field, and has better convergence speed and optimization precision.

Key words: route planning, ant colony optimization, artificial potential, unmanned aerial vehicle

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