Variable Grid‐Based Path‐Planning Approach for UAVs in Air‐Ground Integrated Network

ABSTRACT The operation of Unmanned Aerial Vehicles (UAVs) in low‐altitude airspace is a key component in the Space‐Air‐Ground Integrated Network (SAGIN). Efficient and rational path planning is essential for UAV operations. Existing path‐planning algorithms typically rely on uniform‐grid models based on Cartesian coordinate systems, which seldom account for the unique characteristics of UAV terminal airspaces. UAV terminal airspaces are often defined as cylindrical volumes where multiple UAVs converge at vertiports, allowing for higher operational densities compared to en‐route airspaces. While a fine‐grained grid model is essential for UAV terminal airspace, it is inefficient for en‐route airspace due to excessive computational costs. This paper presents a path‐planning approach based on a variable grid model to find the optimal path while effectively utilizing airspace resources and minimizing computational overhead. Specifically, for the UAV terminal airspace, we propose the Grid‐Optimized A* Path‐Planning (GO‐APP) algorithm, which establishes a sector‐grid model based on a cylindrical coordinate system to find the optimal path. Extending to the en‐route airspace, the Variable‐Grid A* Path‐Planning (VG‐APP) algorithm integrates the GO‐APP and A* algorithms to search the optimal path by stages. Simulations indicate that as the obstacle density in terminal airspace increases from 10% to 40%, GO‐APP demonstrates a path length improvement ranging from 0.78% to 24.46% relative to A*. In generating a path from en‐route airspace to terminal airspace, VG‐APP significantly outperforms A*, reducing path length by up to 15.39% along with improving computational efficiency by 30.54%. Additionally, experiments in the real‐world city of Hangzhou validate the effectiveness of the proposed approach.