Three-Dimension Collision-Free Trajectory Planning of UAVs Based on ADS-B Information in Low-Altitude Urban Airspace

TL;DR

This work tackles the challenge of collision-free 3D UAV trajectory planning in complex, low-altitude urban airspace by leveraging ADS-B information. It introduces a two-tier framework: SSP for coarse routing across a grid-based, multi-layer sub-airspace division, and PSO-RRT for fine-grained trajectory generation within each sub-airspace, incorporating an ADS-B-enabled situational awareness loop. Key contributions include a cost-structured sub-airspace planning objective, a sliding-window dynamic programming mechanism with an attraction-based endpoint guidance, and a PSO-RRT algorithm that fuses RRT/Bi-RRT inputs with particle swarm optimization to minimize trajectory cost while maintaining safety. Simulation results show reduced sub-airspace occupancy, shorter trajectories, and lower cost relative to baseline methods, underscoring the practical potential of ADS-B-informed, 3D UAV planning for urban environments.

Abstract

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.

Figures (15)

• Figure 1: Scenario and airspace division.
• Figure 2: The sliding window in SSP.
• Figure 3: The attraction mechanism in SSP.
• Figure 4: Obstacle models in sub-airspace.
• Figure 5: Trajectory planning in sub-airspace.