City-Wide Low-Altitude Urban Air Mobility: A Scalable Global Path Planning Approach via Risk-Aware Multi-Scale Cell Decomposition

Abstract

The realization of Urban Air Mobility (UAM) necessitates scalable global path planning algorithms capable of ensuring safe navigation within complex urban environments. This paper proposes a multi-scale risk-aware cell decomposition method that efficiently partitions city-scale airspace into variable-granularity sectors based on obstacle proximity and potential risk. Unlike uniform grid approaches or sampling-based methods, our approach dynamically balances resolution with computational speed. Comparative experiments against classical A*, Artificial Potential Fields (APF), and Informed RRT* across diverse urban topologies demonstrate that our method generates significantly safer paths (lower cumulative risk) while reducing computation time by orders of magnitude. The proposed framework, \Larp Path Planner, is open-sourced and integrates directly with OpenStreetMap to facilitate reproducible research in city-wide aerial navigation.