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This study introduces a machine learning-based framework for safe flight planning of aerial vehicles in complex urban environments affected by wind. By training a surrogate model to predict flow fields using building geometry and wind data, the researchers developed a cost-minimizing pathfinder that identifies stable flight trajectories. Experimental validation through flight tests in a wind tunnel shows that the flow-informed approach significantly enhances flight stability and reduces undesired displacement compared to traditional methods.
Flow-informed flight planning can drastically improve aerial vehicle stability in urban environments, reducing unwanted displacement by leveraging real-time wind data.
Advanced air mobility operations hold the potential to enhance and expand regional transportation of both people and goods in populated areas. However, hazardous flight conditions arising from interactions between wind and the built environment remain a significant challenge for aerial vehicles in urban settings. This work proposes a novel framework towards safe flight planning of aerial vehicles in windy urban environments. A learning-based surrogate model is trained to rapidly predict flow fields from readily available information such as building geometry and incident wind. This surrogate prediction is used to calculate a volumetric flight challenge scalar field based on critical flow parameters and proximity to structures. A safe, flow-informed flight trajectory is then identified through a cost-minimizing pathfinder. The complete system is demonstrated experimentally through flight tests of a micro aerial vehicle through a model urban geometry placed in a large fan-array wind tunnel. Comparing this approach to trajectories generated without knowledge of the wind field, we find the flow-informed approach reduces undesired vehicle displacement and improves flight stability. This work is among the first practical demonstrations of safe, wind-aware methodologies for advanced air mobility in urban environments.
