Shenzhen, a major city in southern China, has experienced rapid advancements in Unmanned Aerial Vehicle (UAV) technology, resulting in extensive logistics networks with thousands of daily flights. However, frequent disruptions due to its subtropical monsoon climate, including typhoons and gusty winds, present ongoing challenges. Despite the growing focus on operational costs and third-party risks, research on low-altitude urban wind fields remains scarce. This study addresses this gap by integrating wind field analysis into UAV path planning, introducing key innovations to the classical model. First, UAV wind resistance and turbulence constraints are analyzed, mapping high-wind-speed and turbulence-prone zones in the airspace. Second, wind dynamics are incorporated into path planning by considering airspeed and groundspeed variation, optimizing waypoint selection and flight speed adjustments to improve overall energy efficiency. Additionally, a wind-aware Theta* algorithm is proposed, leveraging wind vectors to expedite search process, while Computational Fluid Dynamics (CFD) techniques are employed to calculate wind fields. A case study of Shenzhen, examining wind patterns over the past decade, demonstrates a 6.23% improvement in groundspeed and a 7.69% reduction in energy consumption compared to wind-agnostic models. This framework advances UAV logistics by enhancing route safety and energy efficiency, contributing to more cost-effective operations.

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%.