基于改进蚁群算法的无人机低空公共航路构建方法
Construction of a UAV Low-altitude Public Air Route based on an Improved Ant Colony Algorithm
查看参考文献30篇
徐晨晨
1,2,3,4
廖小罕
1,3,4
岳焕印
1,3,4
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鹿明
1,3,4
陈西旺
1,3,4
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文摘
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日益增加的无人机数量和飞手自由规划航线给航空安全带来极大隐患。构建一个安全、高效的航空飞行环境,可以为无人机活动设立隔离空域,并在隔离空域内规划无人机低空公共航路,以提高低空空域利用率,为无人机交通管理提供决策依据。本研究充分考虑无人机近地表飞行及其即时通讯等特点,以天津市为例,基于地理信息技术构建以多源地理空间数据为基础的无人机低空飞行环境,包括低空蜂窝网络环境、大气环境和政策空域环境等,并改进传统蚁群算法以搜索无人机最优路径,得到该区无人机低空公共航路网。研究结果表明,改进的蚁群算法大大提高了路径搜索效率,满足无人机航路规划的高时效性、动态更新等要求;并且天津市航路长度符合市场上现有的无人机最远航程要求,基本满足现有的无人机运输要求。本研究描述的无人机低空公共航路研究的核心算法和关键技术,可以为无人机管控系统提供核心技术支撑。 |
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其他语种文摘
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The ever-increasing numbers of UAVs and their free-flying route planning have brought great challenges to national aviation safety.In order to build a safe and efficient aviation flight environment,it is possible to establish an isolated airspace for the UAV activities,and also plan UAV low-altitude public air routes within it.If established,this would increase safe airspace utilization and provide a decision-basis for UAV traffic management.Taking full account of the geographic characteristics of near-surface flight and the near-instant messaging capabilities of UAVs,this study built a low-altitude flight environment for UAVs in Tianjin,China based on multi-source geospatial data using geographic information technologies,and constructed a low-altitude public air route network using an improved Ant Colony Optimization (ACO) algorithm.The study had five major components.Firstly,we developed a path-searching model by improving the traditional ACO algorithm from search space and local target selection.The improved algorithm can be used to search paths in eight directions along a line between the start and end points in order to shorten the search time,and the search radium was determined by an obstacles ratio.Then,local target selection was optimized by introducing evaluation function of A* algorithm and random roulette method.Secondly,we compared the calculating efficiency and path length between the traditional algorithm and the improved one,and found that the improved algorithm was three times more efficient and shorter than the traditional one.Thirdly,the low-altitude flight environment for UAVs included a cellular network,and climatological condition and airspace-policy can be taken into account.The cellular network environment was determined by the distribution of mobile communication base stations and signal attenuation principles.Climatological conditions included wind shear,thunderstorms,glaciation,and low-visibility weather events,and all of which have a significant impact on UAV flight safety.The airspace-policy factors included populated areas,key buildings,and civil airport clearances.Fourthly,we constructed a digital low-altitude airspace by establishing UAV flight principles within air routes and quantifying a grid cost for each kind of constraint.Lastly,the fifth component is verifying the outcomes' reliability by comparing air-route length with the most realistic distance that the UAV currently exhibits.In summary,we found that the improved algorithm greatly shortened search time,and reduced path redundancy.The air-route lengths also comply with the farthest-distance requirement for UAVs currently on the markets.The study described basic ideas and key technologies of the UAV's low-altitude public air route research and can provide the core technical support for the UAV control systems. |
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来源
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地球信息科学学报
,2019,21(4):570-579 【核心库】
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DOI
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10.12082/dqxxkx.2019.180392
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关键词
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无人机
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低空空域
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低空公共航路
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蚁群算法改进
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算法应用
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天津
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地址
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1.
中国科学院地理科学与资源研究所, 资源环境与地理信息国家重点实验室, 北京, 100101
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中国科学院大学, 北京, 100101
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中国科学院无人机应用与管控研究中心, 北京, 100101
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天津中科无人机应用研究院, 天津, 301800
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语种
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中文 |
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文献类型
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研究性论文 |
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ISSN
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1560-8999 |
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学科
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航空 |
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基金
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国家重点研发计划项目
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国家自然科学基金
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文献收藏号
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CSCD:6477397
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