关键词: 无人机, 低空空域, 低空公共航路, 蚁群算法改进, 算法应用, 天津

Abstract:

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.

Key words: UAV, low-altitude airspace, low-altitude public air route, improvement of the ant colony algorithm, algorithm application, Tianjin City