Three-dimension collision-free trajectory planning of UAVs based on ADS-B information in low-altitude urban airspace

The environment of low-altitude urban airspace is complex and variable due to numerous obstacles, non-cooperative aircraft, and birds. Unmanned Aerial Vehicles (UAVs) leveraging environmental information to achieve three-dimension collision-free trajectory planning is the prerequisite to ensure airspace security. However, the timely information of surrounding situation is difficult to acquire by UAVs, which further brings security risks. As a mature technology leveraged in traditional civil aviation, the Automatic Dependent Surveillance-Broadcast (ADS-B) realizes continuous surveillance of the information of aircraft. Consequently, we leverage ADS-B for surveillance and information broadcasting, and divide the aerial airspace into multiple sub-airspaces to improve flight safety in UAV trajectory planning. In detail, we propose the secure Sub-airSpaces Planning (SSP) algorithm and Particle Swarm Optimization Rapidly-exploring Random Trees (PSO-RRT) algorithm for the UAV trajectory planning in law-altitude airspace. The performance of the proposed algorithm is verified by simulations and the results show that SSP reduces both the maximum number of UAVs in the sub-airspace and the length of the trajectory, and PSO-RRT reduces the cost of UAV trajectory in the sub-airspace.