中国安全科学学报 ›› 2021, Vol. 31 ›› Issue (1): 24-29.doi: 10.16265/j.cnki.issn 1003-3033.2021.01.004

• 安全社会科学与安全管理 • 上一篇    下一篇

基于改进蚁群算法的无人机安全航路规划研究

韩鹏 讲师, 张冰玉 讲师   

  1. 中国民航大学 空中交通管理学院,天津 300300
  • 收稿日期:2020-10-13 修回日期:2020-12-07 发布日期:2021-08-18
  • 作者简介:韩 鹏 (1991—),男,山东济宁人,博士,讲师,主要从事空中交通管理、无人机安全风险评估等方面的研究。E-mail:p_han@cauc.edu.cn。
  • 基金资助:
    天津市教委科研计划项目(2019KJ128)。

Safety route planning of UAV based on improved ant colony algorithm

HAN Peng, ZHANG Bingyu   

  1. School of Air Traffic Management, Civil Aviation University of China, Tianjin 300300, China
  • Received:2020-10-13 Revised:2020-12-07 Published:2021-08-18

摘要: 为减少无人机(UAV)坠毁伤人事故发生,首先,通过UAV飞行空域栅格化,以每飞行小时地面人员伤亡数量为指标定义栅格风险因子,创建航路安全代价期望函数;然后,考虑UAV航路代价,探究距离和安全双重约束条件下的航路规划方法,并采用改进蚁群算法规划最优航路;最后,通过城市物流UAV配送场景验证该模型的有效性,并对比是否考虑安全因素对规划航路结果的差异。结果表明:考虑安全因素的规划航路飞行距离和飞行时间增加在30%以内,但航路总行程伤亡人员数量降低可达60%。

关键词: 改进蚁群算法, 无人机(UAV), 航路规划, 安全代价, 栅格风险因子

Abstract: In order to reduce UAV crash accidents, firstly, air route safety cost function was established with casualty of ground personnel per flight hour as index to define risk factor of grids through UAV air space gridding Then, route planning method under double restraints of distance and safety was explored considering UAV route cost, and optimal route was planned based on improved ant colony algorithm. Finally, validity of the planning model was verified by urban UAV logistics distribution scenario before difference in planning results with safety factors being taken into account or not.was compared.The results indicate that the air route planning model considering such factors can improve flying distance and time by within 30%, but the casualty rate through the whole route can be reduced by as much as 60%.

Key words: improved ant colony algorithm, unmanned aerial vehicle (UAV), route planning, safety cost, grids risk factor

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