PROGRESS IN GEOGRAPHY ›› 2021, Vol. 40 ›› Issue (9): 1516-1527.doi: 10.18306/dlkxjz.2021.09.007

• Operation Supervision of UAV • Previous Articles     Next Articles

Dynamic path planning of unmanned aerial vehicle based on crowd density prediction

JIAO Qingyu(), CHEN Xinfeng*(), ZHENG Zhigang, BAI Yiqin, LIU Yansi, ZHANG Zhengjuan, SUN Longni   

  1. China Academy of Civil Aviation Science and Technology, Beijing 100028, China
  • Received:2020-12-29 Revised:2021-03-30 Online:2021-09-28 Published:2021-11-28
  • Contact: CHEN Xinfeng;
  • Supported by:
    National Key Research and Development Program of China(2017YFB0503005);The Key Research Program of Chinese Academy of Sciences(ZDRW-KT-2020-2-1);Tianjin Intelligent Manufacturing Project(Tianjin-IMP-2018-2)


With the rapid growth of the number of unmanned aerial vehicles (UAVs), in order to assess the risk of people on the ground especially in cities, improve the operational efficiency of UAV, and reduce the threat of UAV to the crowd, it is necessary to make a specific path planning for UAVs based on the crowd density. However, static demographic data are still used as the ground risk analysis basis for the path planning of UAV, and real-time path planning of UAV according to the spatial-temporal characteristics of crowd density are often not performed. This study first analyzed the characteristics of urban road network crowd density based on the spatiotemporal data. Second, the convolutional neural network-long short-term memory (CNN-LSTM) combined model (C-Snet model) was established to predict the population density in different urban areas. Finally, the improved A* algorithm was used for real-time path planning and risk assessment of UAV according to the predicted crowd density data. The results show that the risk of UAV operation is reduced by 76%, which can provide a theoretical reference for the development of real-time path planning function of UAV traffic management system.

Key words: UAV traffic management, risk assessment, CNN, crowd density, path planning