Computational modeling demonstrates dynamic wind profiles alter low-altitude drone travel time and energy use, indicating departure timing and altitude selection optimize urban flight efficiency.
This paper develops a wind-aware routing framework for low-altitude small unmanned aerial vehicles (UAVs) operating under FAA Part 107 with maximum takeoff weight under 55 lb and altitudes at or below 400 ft above ground level, and evaluates how spatiotemporal wind influences travel time and energy consumption. The framework integrates wind-vector projections into a four-dimensional time-expanded network and applies a time-dependent Pareto-label-correcting algorithm to generate non-dominated routes between drone ports (i.e., hubs/staging nodes). Controlled experiments vary wind heading, vertical shear, and spatial heterogeneity in a representative urban corridor to isolate the role of wind structure. Results show that tailwinds simultaneously reduce travel time and energy, headwinds increase both, and time-varying winds shift the relative efficiency of early versus late departures. The findings highlight that ignoring wind can substantially increase energy consumption or lead to delay penalties. The framework provides UAV operators with actionable guidance on departure timing and altitude selection and establishes a reproducible benchmark for assessing air mobility corridor performance under realistic wind conditions.
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Ahmed et al. (2026) studied this question.
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