Dynamic Airspace Management for UAVs in Evolving Urban Environments: Collaborative Coordination and Human Safety

2026-07-06

AI Summary

This work addresses the challenges of collision avoidance and human safety for low-altitude drone operations in complex urban and densely populated areas. The authors propose Pharos, a system that strikes a balance between distributed sensing and centralized control to enable safe, parallel flight in shared airspace through multi-agent coordination. Its key innovation lies in being the first to explicitly model human fear as a factor in airspace coordination and to introduce spatial entropy as a quantitative metric for airspace utilization efficiency. Evaluations using 3D simulations based on real-world urban data demonstrate that Pharos reduces human fear by 52.72% compared to the Ipopt baseline, while improving spatial entropy by 70.82% over Ipopt and by 2.03% over A*, thereby significantly enhancing both safety and airspace efficiency.

Abstract

The low-altitude economy is an emerging industry with significant development potential, in which the safety of unmanned aerial vehicle (UAV) operations is a critical challenge. Particularly within complex urban topographies and human-populated environments, UAV airspace management must prioritize collision avoidance and human safety. We propose Pharos, a collaborative multi-UAV airspace management system. Pharos lies between the distributed local perception paradigm and the centralized fine-grained control paradigm. Pharos coordinates the safe parallel execution of UAVs in shared airspace while innovatively accounting for the impact of human fear. Pharos is implemented using the MAPPO algorithm due to its faster convergence and higher rewards than other typical MARL algorithms (HAPPO and HATRPO). To evaluate Pharos, we developed a 3D simulation system using real urban data. Visualization results demonstrate its effective airspace coordination capability. Regarding performance verification, Pharos reduced human fear by 52.72% compared to the benchmark Ipopt. Moreover, we designed spatial entropy as a system evaluation metric to quantify space utilization, which improved performance by 70.82% and 2.03% compared to the benchmarks Ipopt and A-star, respectively. The source code is available at an anonymized repository: https://github.com/pharos-anonymized/source-code.git.

Problem

• UAV airspace management
• urban environments
• human safety
• collision avoidance
• low-altitude economy

Innovation

• multi-UAV coordination
• human-aware airspace management
• MAPPO
• spatial entropy
• urban UAV safety