Wind-Informed Rapid Flight-Planning in Complex Urban Topologies via Machine Learning and Experimental Validation

2026-08-10

AI Summary

This study addresses the hazardous airflow patterns generated by building-wind interactions in urban environments, which pose significant risks to low-altitude aerial vehicles. The work proposes the first end-to-end system that integrates real-time wind field prediction with trajectory planning. Leveraging building geometry and incoming wind data, a machine learning surrogate model rapidly predicts three-dimensional urban wind fields and constructs a scalar field representing flight challenge levels that account for aerodynamic disturbances. Building upon this field, a cost-minimizing path planner generates safe trajectories. Wind tunnel experiments demonstrate that, compared to conventional approaches ignoring wind effects, the proposed method substantially reduces unintended vehicle deviations and enhances flight stability, enabling micro aerial vehicles to safely navigate through complex urban settings.

Abstract

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.

Problem

• urban wind
• flight planning
• aerial vehicles
• flow field
• advanced air mobility

Innovation

• wind-informed flight planning
• machine learning surrogate model
• urban airflow prediction
• volumetric challenge field
• experimental validation