Low-altitude drones have great potential for development in urban operations, emergency rescue, logistics delivery and airspace coordination. They are a vital component of the low-altitude economy and intelligent aviation systems. However, low-altitude regions are subject to highly complex aerodynamic conditions influenced by multiple factors, such as terrain, buildings and other airborne objects, compared to mid-to-high altitude flight environments. These environments exhibit strong flow field unsteadiness, diverse disturbance sources and significant coupling effects, which impose higher demands on UAV (unmanned aerial vehicle) aerodynamic performance, flight stability and operational safety. Therefore, systematic research into UAV aerodynamics in low-altitude environments is highly valuable from both theoretical and engineering perspectives. Such research deepens our understanding of the aerodynamic mechanisms in complex, non-uniform flow fields, supports the optimization of UAV design and control methods, and enhances the safety and environmental adaptability of typical low-altitude applications.

Extensive research has been conducted on the aerodynamic issues of UAVs operating in low-altitude environments, with particular emphasis on complex wind fields, confined environments, and multi-UAV scenarios. Studies on low-altitude complex wind fields indicate that terrain variations, building configurations, and thermal effects lead to pronounced spatial non-uniformity, strong velocity shear, and highly unsteady turbulent characteristics. In urban environments, wind–building interactions generate flow separation, recirculation, and wake vortices, forming multi-scale flow structures that significantly disturb UAV lift, thrust, and attitude stability. Owing to strong multi-scale coupling and the diversity of urban geometries, accurate and universally applicable models for rapid wind field prediction remain challenging; recent progress has therefore focused on integrating CFD (computational fluid dynamics) with data-driven approaches, particularly machine learning, to accelerate wind field prediction and analyze UAV disturbance responses.

Regarding low-altitude confined environments, existing research has mainly addressed boundary-induced aerodynamic effects. Ground effect has been extensively investigated, and aerodynamic models describing rotor–ground interactions have been established and validated through experiments and numerical simulations. With the expansion of UAV operations into indoor and structurally complex environments, increasing attention has been paid to ceiling and sidewall effects. However, studies involving inclined surfaces, finite-sized boundaries, and multi-wall coupling remain limited, and most safety and performance analyses are still based on simplified single-boundary assumptions.

In low-altitude multi-UAV environments, research has primarily focused on aerodynamic interference caused by rotor wakes and downwash interactions. CFD simulations are widely used to analyze wake evolution and induced velocity distributions, with experiments providing partial validation. Formation flight represents one of the most aerodynamically sensitive scenarios, as wake interference alters aerodynamic load distributions and challenges formation stability. Although some studies have incorporated aerodynamic interactions into formation control and path planning, most remain qualitative or restricted to specific configurations, and systematic quantitative models with broad validation are still lacking.

Based on the existing body of research, it can be concluded that the low-altitude aerodynamic environment is a critical factor affecting the flight performance, stability, and operational safety of UAVs. Complex low-altitude wind fields exhibit strong spatial non-uniformity, pronounced unsteadiness, and multi-scale coupling, and their interactions with terrain and built environments generate persistent disturbances in aerodynamic loads and control responses. In confined environments, boundary-induced interference significantly alters rotor flow structures and aerodynamic characteristics, although most studies remain based on idealized single-boundary assumptions. In multi-UAV environments, aerodynamic coupling caused by rotor wakes and downwash plays a key role in cooperative flight stability, yet existing research is largely limited to mechanism analysis and qualitative assessment. Overall, despite progress in flow physics and numerical modeling, unified high-fidelity models, comprehensive experimental validation, and effective integration with flight control and mission planning under realistic low-altitude conditions are still lacking.

Future research on UAV aerodynamics in low-altitude environments should focus on improving modeling fidelity, experimental consistency, and integration with control strategies. For complex low-altitude wind fields, the development of multi-scale modeling and high-fidelity numerical simulation methods remains essential for clarifying the impact of unsteady flows on UAV aerodynamic and control performance. Meanwhile, standardized experimental platforms are required to ensure consistency among wind field measurements, sensor calibration, and flight tests, and data-driven prediction and adaptive control approaches are expected to enhance UAV resilience to wind disturbances.

In confined environments, priority should be given to the development of accurate and broadly applicable aerodynamic models for complex boundary-induced interference, coupled with UAV dynamic models to enable systematic safety assessments. The identification of safety corridors based on collision risk evaluation would further support stable UAV operations in highly constrained spaces.

For multi-UAV environments, integrated research frameworks combining high-fidelity CFD simulations, experimental validation, and data-driven modeling are needed to advance the understanding of aerodynamic coupling mechanisms and to support cooperative control design. In particular, adaptive control methods based on artificial intelligence and reinforcement learning hold significant potential for improving robustness and operational efficiency in large-scale UAV formations under strong disturbances and uncertainty.