A Dual-Field Framework for Urban Low-Altitude UAV Traffic Planning and Management
Zhaoqi Dong, Lei Chen
AI summary
Problem
Current urban UAV traffic management systems rely on isotropic cost metrics that fail to jointly account for directional traffic flow and spatially variant environmental risks, leading to unsafe or inefficient trajectories in cluttered airspace.
Approach
The framework couples a spatial risk traversability field with a macroscopic traffic potential field to compute geodesic UAV paths via an anisotropic metric, updated locally for real-time conflict resolution.
Key results
- Negligible exposure to high-risk regions
- Substantial reduction in average path curvature
- 6.3× computational speedup via local field updates
- High macroscopic flow adherence (0.989) in randomized environments
Why it matters
Provides a scalable, safety-aware computational foundation for future Unmanned Traffic Management (UTM) systems and urban air mobility operations.
Abstract
Urban Air Mobility emerges as a transformative mode of transportation, but its integration into complex low– altitude urban environments requires systematic consideration of safety and efficiency. This study aims to develop a compu- tational framework that enables structured traffic organization while accounting for spatially variant risks. The framework introduces a dual–field environmental model that couples a traversability field, which quantifies continuous anisotropic risk, with a scalar potential field, which encodes macroscopic traffic flow. The path planning formulation computes geodesics under an anisotropic metric derived from the dual–field, and the cen- tralized coordination mechanism updates the fields to maintain real–time deconfliction. Simulation results demonstrate that the proposed framework generates paths that reduce exposure to high–risk regions to a negligible level and achieve a substantial reduction in average curvature compared to a baseline planner. Furthermore, the local update mechanism provides significant computational speedup for dynamic real–time scenarios. These results validate the capability of the dual–field framework to unify safety and efficiency in urban airspace management, providing a scalable foundation for future unmanned traffic management systems.