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

TL;DR

This paper presents a novel approach for UAV collision-free trajectory planning in low-altitude urban airspace using ADS-B surveillance data, combining sub-airspace division with SSP and PSO-RRT algorithms to enhance safety and efficiency.

Contribution

It introduces a new framework utilizing ADS-B data for UAV trajectory planning, including the SSP and PSO-RRT algorithms for improved safety and reduced trajectory costs.

Findings

SSP reduces maximum UAVs in sub-airspaces and trajectory length

PSO-RRT decreases UAV trajectory cost

Simulation results validate the effectiveness of the proposed algorithms

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.