Small unmanned aerial vehicles path planning method considering urban low-altitude wind fields

The operational safety of drone routes has gained more attention as unmanned aerial vehicles (UAVs) are used extensively in urban logistics. Addressing the safety and efficiency issues caused by urban wind fields, this paper integrates the operational characteristics of logistics drones and employs computational fluid dynamics (CFD) to estimate urban wind fields. The impact of wind fields on UAVs is analyzed from the perspectives of flight safety, variations in drone speed, and turbulence zones around buildings. A path planning model is constructed, and an improved Theta* algorithm is proposed. The study takes the central business district of Longgang District, Shenzhen, as the research scenario. Simulations of urban wind fields are conducted based on historical prevailing winds to delineate hazardous flight regions and the influence range of obstacles within the wind field. The proposed method is then used to plan drone routes under different wind field conditions. Results show that the constructed path planning model effectively identifies high wind speed and turbulent areas within the airspace. When compared to routes designed to minimize distance, the drone route saves flying time by 9.27% by taking into account how wind fields affect drone speed and avoiding no-fly zones. Additionally, the proposed algorithm outperforms traditional algorithms in terms of route distance and flight time, and reduces the number of turns by 31.78% and computation time by 62.63%.