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

TL;DR

This paper introduces a scalable, risk-aware multi-scale cell decomposition method for city-wide urban air mobility path planning, improving safety and efficiency over traditional approaches.

Contribution

It presents a novel multi-scale risk-aware cell decomposition approach that efficiently balances resolution and speed for large-scale urban airspace navigation.

Findings

Safer paths with lower risk compared to classical methods

Significant reduction in computation time

Effective handling of complex urban environments

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, 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.