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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2404.18436 |
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Table of Contents:
- 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.