城市级低空城市空中交通：一种通过风险感知多尺度单元分解的可扩展全局路径规划方法
City-Wide Low-Altitude Urban Air Mobility: A Scalable Global Path Planning Approach via Risk-Aware Multi-Scale Cell Decomposition
AI总结：
本文提出一种基于风险感知的多尺度单元分解方法，用于城市级低空城市空中交通的可扩展全局路径规划，通过动态平衡分辨率与计算速度，降低累计风险并减少计算时间。
摘要：
城市空中交通（UAM）的实现需要可扩展的全局路径规划算法，以确保在复杂城市环境中安全导航。本文提出了一种多尺度风险感知单元分解方法，能够高效地将城市级空域划分为不同粒度的扇区，并根据障碍物接近度和预期风险为每个单元分配解析估计的风险值。与均匀网格方法或采样方法不同，我们的方法通过马哈拉诺斯距离投影限制单元风险，从而消除耗时的现场采样。在五个不同的城市地形上，与经典A*、人工势场（APF）和指导RRT*的比较实验表明，我们的方法生成更安全的路径，累积风险更低，同时计算时间减少了多个数量级。所提出的框架Larp Path Planner已开源，通过其修改的GeoJSON内部表示支持任何地图提供商，并使用OpenStreetMap数据进行实验，以促进城市级空中导航的可重复研究。
英文摘要：
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.
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