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![Fig. 1 Urban city model in wind tunnel experiment [3]](profile/Harald-Pfifer/publication/360816113/figure/fig1/AS:1159248646021120@1653397823568/Urban-city-model-in-wind-tunnel-experiment-3_Q320.jpg)
A general approach is presented to compute energy optimal flight paths for unmanned aerial vehicle (UAV) in urban environments. In order to minimize the energy consumption, the flight path is optimized by exploiting local wind phenomena, i.e., upwind and tailwind areas from the airflow around buildings. The approach is demonstrated on a delivery UA...
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... This size offered the best trade-off between computational time and accurate resolution of flow effects. The same setup was used for the different test cases described in Section 2 with the only difference being a rotation of the building area to implement the different wind directions. The setup of the topography for the scenarios is depicted in Fig. 3. PALM calculates the velocity components u, v, w in the Arakawa C-grid [19]. This means that each values for u, v and w do not refer to the same grid point, but to the appropriate side face of the volume mesh. The output data of the wind field calculation in Arakawa C-grid is converted into velocity point data. Each knot of grid volume ...
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... no knowledge of the current wind field, simply cannot differentiate between energy differences of paths of the same length. Hence, even with the same flight distance the energy optimization can achieve an energy reduction. Note that the difference of shortest way in 63.435 • -scenario to the other ones is due to resolution of rotated grid, see Fig. 3. An example of the actual flight paths is given in Fig. 7, where orange is the shortest path and blue the lowest energy consumption. Note that most of the energy savings actually come from exploiting regions of upwind and not tailwinds. This becomes clear when looking at the upwind along the trajectories, see Fig. 8. The energy ...
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... fields than the shortest way. If one look at Eq. (8), it can be seen that small differences of upwind have a greater impact on Table 4 Comparison between shortest-way-optimization and energy-optimization, wind speed 0.28 m/s, direction 180 • 180 • 180 • . Difference of shortest way to 63.435 • -scenario due to resolution of rotated grid, see Fig. 3. energy consumption than small ones of tailwinds. Furthermore, it must be said that no path smoothing is considered. The flight path is just allowed from grid point to adjacent grid point. Hence there are some kinks where a direct line is expected. A flight path comparison for this scenario is presented in Fig. 9 with the shortest path ...
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This paper presents a general approach to compute energy optimal flight paths for unmanned aerial vehicle (UAV) in urban environments. To minimize the energy required, the flight path is optimized by exploiting local wind phenomena, i.e., upwind and tailwind areas from the airflow around buildings. A realistic wind field of a model urban environment typical for continental Europe is generated using PALM, a Large Eddy Simulation tool. The calculated wind field feeds into the flight path planning algorithm to minimize the energy required. A specifically tailored A-Star-Algorithm is used to optimize flight trajectories. The approach is demonstrated on a delivery UAV benchmark scenario. Energy optimal flight paths are compared to shortest way trajectories for 12 different scenarios. It is shown that energy can be saved significantly while flying in a city using knowledge of the current wind field.
