  The environment of low-altitude urban airspace is complex and variable due to
numerous obstacles, non-cooperative aircrafts, 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 aircrafts. 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.
