City-Wide Low-Altitude Urban Air Mobility: A Scalable Global Path Planning Approach via Risk-Aware Multi-Scale Cell Decomposition
This work addresses the scalability and safety of global path planning for UAM in complex urban environments, offering a practical solution for city-wide aerial navigation.
The paper proposes a multi-scale risk-aware cell decomposition method for global path planning in Urban Air Mobility that reduces computation time by orders of magnitude while generating safer paths with lower cumulative risk compared to A*, APF, and Informed RRT* across five urban topologies.
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, assigning each cell an analytically estimated risk value based on obstacle proximity and expected risk. Unlike uniform grid approaches or sampling-based methods, our approach dynamically balances resolution with computational speed by bounding cell risk via Mahalanobis distance projections, eliminating exhaustive field sampling. Comparative experiments against classical A*, Artificial Potential Fields (APF), and Informed RRT* across five diverse urban topologies demonstrate that our method generates safer paths with lower cumulative risk while reducing computation time by orders of magnitude. The proposed framework, Larp Path Planner, is open-sourced and supports any map provider via its modified GeoJSON internal representation, with experiments conducted using OpenStreetMap data to facilitate reproducible research in city-wide aerial navigation.