# Generic Component-Based Mission-Centric Energy Model for Micro-Scale Unmanned Aerial Vehicles
## 4 Experiments
### Results
Figure 4a shows the energy predictions of the same four models as used for the indoor flights. Interestingly, the sudden jump of the _Vertical Model_ is gone. We suspect the height to be internally adapted by the used autopilot software. In the outdoor case, this was prevented by the used GPS receiver and/or the used barometer. Another explanation is the stronger variability of the flights because of environmental effects, which are not existent indoors. All models behave in a very similar manner for the outdoor flight and, similarly to the indoor flights, the _Hover Model_ underestimates the used energy because it ignores movement. As visible in Figure 4c,d, for outdoor flights 2 and 3 the _Hover Model_ behaved best, which may be an indication that the environment (wind) supported the movement and therefore reduced the energy needed for movement. This theory is supported by the performance of the _Horizontal Model_, which shows in all flights the largest error. Figure 4b-d show a generic trend of the models to converge to a static prediction error most of the time as visible in all flights for the _Combined_ and _Vertical Models_. The _Horizontal Model_ seems to be not as stable in the long run. This may be caused by the batteries providing different voltages over time, which necessitates an increase in throttle, which is not taken into account by the _Horizontal Model_. The performance of the _Hover Model_ differs strongly between the individual flights, which indicates that the model is very sensitive to environmental conditions or the condition of the battery. However, it still shows on average good performance. The _Combined Model_, which was very good for the Indoor Validation Flights, showed a max. accumulated error of 6.316% with a max. deviation of power estimation of 0.234 \(W\). Consequently, this model can be considered robust against slight environmental and minor platform changes, because the outdoor scenario was very favourable with nearly no wind and a very low flight height. For the short outdoor flights, the _Hover Model_ performed even better with a deviation of 2.71% and 0.1306 \(W\). This model performed reasonably well for the _Indoor Validation Flights_. Consequently, we can assume that the extended capabilities of modelling complex energy consumption of the _Combined_ and _Vertical Model_ create a stronger link to the platform specifics. The _Hover Model_, which uses less specifics of the platform, creates a more general prediction of the energy consumption of a copter. This is in line with the findings of [PERSON] et al. (2018). \begin{table}
\begin{tabular}{c c c|c c c c c} \hline \hline
**Validation** & \multicolumn{4}{c}{**Flight 1**} & \multicolumn{4}{c}{**Flight 2**} \\
**Model** & **Hover** & **Vert** & **Horiz** & **Comb** & **Hover** & **Vert** & **Horiz** & **Comb** \\ \hline mean & 0.0866 & 0.2231 & 0.0280 & 0.0362 & 0.0218 & 0.1087 & 0.0367 & 0.0186 \\ std & 0.0728 & 0.2311 & 0.0164 & 0.0277 & 0.0121 & 0.1188 & 0.0104 & 0.0076 \\ \hline
25\%-quantile & 0.0128 & 0.0126 & 0.0151 & 0.0181 & 0.0121 & 0.0132 & 0.0341 & 0.0124 \\
50\%-quantile & 0.0699 & 0.0675 & 0.0286 & 0.0253 & 0.0220 & 0.0305 & 0.0391 & 0.0199 \\
75\%-quantile & 0.1487 & 0.4287 & 0.0377 & 0.0542 & 0.0322 & 0.2346 & 0.0437 & 0.0249 \\ \hline max & 0.2284 & 0.6310 & 0.0686 & 0.1046 & 0.0448 & 0.3544 & 0.0502 & 0.0315 \\ \(\sum\Delta e\) & 4.4622 & 12.4032 & 1.3300 & 2.0428 & 0.9950 & 10.3208 & 1.3155 & 0.4166 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance results of the four generated energy estimation models applied to the two indoor validation flights. Dark green fields highlight best results. Light green fields highlight second best results. Figure 4: Estimation results and errors of differently trained models tested in three outdoor validation flights. The top left figure (**a**) shows the measured and predicted energy values of the used quadcopter for the first outdoor validation flight.