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计算机工程 ›› 2011, Vol. 37 ›› Issue (4): 206-209. doi: 10.3969/j.issn.1000-3428.2011.04.074

• 人工智能及识别技术 • 上一篇    下一篇

多雷达威胁环境下的无人机路径规划

章国林,李 平,韩 波,郑 巍   

  1. (浙江大学工业控制研究所,杭州 310027)
  • 出版日期:2011-02-20 发布日期:2011-02-17
  • 作者简介:章国林(1986-),男,硕士,主研方向:无人机导航; 李 平,教授、博士生导师;韩 波,副研究员;郑 巍,本科生
  • 基金资助:
    国家“863”计划基金资助项目“基于自主无人飞行器的田间作物遥感信息采集系统的研制与应用”(2006AA10Z204)

Unmanned Aerial Vehicle Path Planning Under Multi-radar Threatening Environment

ZHANG Guo-lin, LI Ping, HAN Bo, ZHENG Wei   

  1. (Institute of Industrial Process Control, Zhejiang University, Hangzhou 310027, China)
  • Online:2011-02-20 Published:2011-02-17

摘要: 根据雷达对无人机的瞬时探测概率模型以及无人机的运动特性,提出一种基于改进蚁群算法与Voronoi图相结合的无人机路径规划方法,使无人机突破雷达威胁环境的路径成本最低。将该方法与其他路径规划方法在所得路径燃油成本、威胁成本、总成本以及计算时间方面进行对比,表明该方法具有更低的路径成本和更少的计算时间。

关键词: 路径规划, 蚁群算法, Voronoi图, 无人机

Abstract: According to the radar for unmanned aerial vehicle instantaneous detection probability model and motion characteristics of unmanned aerial vehicle, this paper presents an unmanned aerial vehicle path planning method based on improved Ant Colony Algorithm(ACA) and Voronoi diagram in order to minimize the path cost when unmanned aerial vehicle breaks through the threaten field with multiple radars. Compared with other three kinds of path planning methods in income fuel costs, threat path cost, total costs and computing time, this method has lower path cost and less computing time.

Key words: path planning, Ant Colony Algorithm(ACA), Voronoi diagram, unmanned aerial vehicle

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