Energy Optimal Flight Path Planning for Unmanned Aerial Vehicles in Urban Environments Considering Trajectory Uncertainties

This paper introduces a comprehensive approach for computing energy-efficient flight trajectories for unmanned aerial vehicles (UAVs) while considering trajectory uncertainties. The specific locations and environmental conditions under which the UAV will operate are inherently uncertain. Our goal is to minimize the sensitivity to these uncertainties in order to mitigate potential energy losses. The primary optimization objective is to minimize energy consumption by exploiting local wind phenomena, while accounting for negative effects of drift and turbulence. The flight path planning algorithm uses a precalculated time-averaged wind field to optimize the flight path and a time-dependent wind field to account for turbulent airflow dynamics. To address the optimization sensitivity to uncertainties, a specialized cost function is integrated into the A-star Algorithm, a type of branch-and-bound optimizer. Three distinct uncertainties are independently established for optimization: local drift, reduced upwind due to vortices, and turbulence avoidance. The key strategies applied address these uncertainties to achieve energy-efficient flight paths with reduced sensitivity. The proposed approach is demonstrated using a benchmark scenario involving a delivery UAV. Optimized flight trajectories are compared against shortest path trajectories. The results demonstrate significant energy saving potential when flying in urban areas by exploiting knowledge of the current wind conditions and minimizing the effects of uncertainties.