Iterative construction of low-altitude UAV air route network in urban areas: Case planning and assessment

Chenchen Xu, Xiaohan Liao, Huping Ye, Huanyin Yue
2020 Journal of Geographical Sciences  
With the rapid increase of Unmanned Aircraft Vehicle (UAV) numbers, the contradiction between extensive flight demands and limited low-altitude airspace resources has become increasingly prominent. To ensure the safety and efficiency of low-altitude UAV operations, the low-altitude UAV public air route creatively proposed by the Chinese Academy of Sciences (CAS) and supported by the Civil Aviation Administration of China (CAAC) has been gradually recognized. However, present planning research
more » ... UAV low-altitude air route is not enough to explore how to use the ground transportation infrastructure, how to closely combine the surface pattern characteristics, and how to form the mechanism of "network". Based on the solution proposed in the early stage and related researches, this paper further deepens the exploration of the low-altitude public air route network and the implementation of key technologies and steps with an actual case study in Tianjin, China. Firstly, a path-planning environment consisting of favorable spaces, obstacle spaces, and mobile communication spaces for UAV flights was pre-constructed. Subsequently, air routes were planned by using the conflict detection and path re-planning algorithm. Our study also assessed the network by computing the population exposure risk index (PERI) and found that the index value was greatly reduced after the construction of the network, indicating that the network can effectively reduce the operational risk. In this study, a low-altitude UAV air route network in an actual region was constructed using multidisciplinary approaches such as remote sensing, geographic information, aviation, and transportation; it indirectly verified the rationality of the outcomes. This can provide practical solutions to low-altitude traffic problems in urban areas.
doi:10.1007/s11442-020-1798-4 fatcat:lc7ah2mcgjcmdpfvlil4q7dhgq