A Dual-Field Framework for Urban Low-Altitude UAV Traffic Planning and Management

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 computational 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 centralized 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.