# Generic Component-Based Mission-Centric Energy Model for Micro-Scale Unmanned Aerial Vehicles
## 5 Conclusions  
In this paper, we showed a novel generic energy modelling approach for quadcopters. The approach provides predictions of the necessary energy for whole missions, but also arbitrary small parts of missions. The generic model was applied to the FINken3 quadcopters used in the SwarmLab. The process of applying the model to a specific hardware platform was shown, including the necessary experiments and the data analysis needed to infer the parameters of the model. We created four models using different amounts of training data using expert knowledge and fitted the models with training data from test flights. In the end, we verified these models against additional validation flights within and out of our laboratory to evaluate the accuracy of the predictions. The resulting accuracy of the best model showed an error of less than 1.4% for the indoor validation on the same copter, which was used to train the model. This qualifies the model and the model-fitting process to be used to optimise missions of quadcopters both online and offline. Results using another copter in a different environment still showed an error less than 6.5% if considering the best indoor model. The selection of the best overall model reduces the error to 4.46% for the indoor validation flights and 2.71% for the outdoor validation flights. The usage of another quadcopter for the outdoor flights also showed the generalisation achievable by the machine learning approach, which eases the generation of energy-models for fleets of similar quadcopters. The time the quadcopter spends flying turned out to be by far the most important factor in energy consumption. Our experiments show that the constant energy consumption of hovering predicts the energy consumption in the validation flights with less than 5% error for short flights. In the future, we aim to extend our model by a battery component model and a specific outdoor model to incorporate the effects of the environment like wind. Additionally, we want to extend the mission parameters of our implementation of the generic model to enable additional handling of heterogeneous swarms of quadcopters. Furthermore, we want to combine the model with energy-aware swarm behaviour algorithms to evaluate the resulting benefits. Finally, we aim to use the model in an evolutionary algorithm to optimise the set-up of our quadcopter platform as well as the usage of components of each quadcopter to minimise the global energy usage of fleets or swarms of quadcopters. Conceptualization C.S. and S.P. ; methodology S.P. ; software C.S., S.P., S.M. ([PERSON]); validation C.S. and S.M. ([PERSON]); formal analysis C.S. and S.P. ; resources S.M. ([PERSON]); data curation S.P. ; writing-original draft preparation C.S., S.M. ([PERSON]), S.P. ; writing-review and editing C.S. and S.M. ([PERSON]); visualization S.P. ; supervision S.M. ([PERSON]); project administration S.M. ([PERSON]); All authors have read and agreed to the published version of the manuscript. \begin{table}
\begin{tabular}{c c|c c|c c|c c c} \hline \hline
**Validation** & \multicolumn{3}{c}{**Flight 1**} & \multicolumn{3}{c}{**Flight 2**} & \multicolumn{3}{c}{**Flight 3**} \\
**Model** & **Hover** & **Vert** & **Comb** & **Hover** & **Vert** & **Comb** & **Hover** & **Vert** & **Comb** \\ \hline mean & 0.0456 & 0.0116 & 0.0317 & 0.0597 & 0.1127 & 0.1024 & 0.0555 & 0.0964 & 0.1373 \\ std & 0.0294 & 0.0077 & 0.0218 & 0.0197 & 0.0353 & 0.0313 & 0.0281 & 0.0509 & 0.0712 \\ max & 0.1306 & 0.0293 & 0.0648 & 0.0833 & 0.1670 & 0.1504 & 0.1094 & 0.1588 & 0.2340 \\ \(\sum\Delta e\) & 2.7085 & 0.3777 & 1.2639 & 0.1630 & 3.1661 & 3.2976 & 2.1741 & 4.1327 & 6.3156 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance results of three of the four generated energy estimation models applied to the three outdoor validation flights. The _Horizontal Model_ did not produce good results and is omitted for clarity. Dark green fields highlight best results. Light green fields highlight second best results. **Funding:** This research received no external funding.