# Generic Component-Based Mission-Centric Energy Model for Micro-Scale Unmanned Aerial Vehicles
## 4 Experiments
### Experimental Set-Up  
We conducted multiple experiments to parametrise and validate the model against the real-world behaviour of the copters. To estimate _Constant Power Functions_, we disassembled the quadcopter into the components and directly measured the power consumption of each component. The measurements of the Constant Power Functions delivered the results shown in Figure 2. The power consumption in the relevant grey areas is clearly constant. Changes of power consumption exists for some components like the 802.15.4 Transceiver because of internal boot behaviour. Therefore, only the stable part of experimental data was used. The parameters were estimated using averaging on the whole selected sample subset. The resulting parameters are shown in Table 1.  
Figure 2: Results of the static power measurements of the Components. The grey area indicates the used samples to derive the parameters. Multiple grey areas indicate different areas used for different Constant Power Functions within a Discrete Power Function. The parameters of the _Motor_ component need data gathered mid-flight using fully assembled quadcopters. To this end, we switched to the integrated current sensor of the FINken3 platform and logged the voltage, current and movement commands to an on-board SD-card. This allowed us to gather data with high speed. Internally, the FINken is capable of measuring the voltage of the Lithium-Polymer (LiPo) battery using the Analogue Digital Converter (ADC) of the micro-controller. The measurement has a precision of \(\pm\frac{18.3\,\mathrm{V}}{4096}=\pm 0.0045\,\mathrm{V}\). The current is measured using an external Allegro ACS/23 LLCTR-40 AB-T, which also uses the internal ADC of the autopilot. The current sensor needs to be calibrated, which limits its actual precision from the theoretical precision of \(\pm\frac{40\,\mathrm{A}}{4096}=\pm 0.001\,\mathrm{A}\). We considered multiple flight situations representing relevant areas of the parameter space. To enable position estimation of the quadcopter in the experiment, we use the on-board height sensor to estimate the \(z\) coordinate and a specialised tracking system installed in our lab, which is based on CNNs [21]. The precision of the tracking system is approximately \(\pm 15\,\mathrm{\ cm}\). Additionally, we logged the gyroscope and acceleration data of the internal IMU, which is a MCU-6000 featuring \(\pm 16\,\mathrm{g}\) sensor range with a sensitivity of \(\frac{1}{2048}\,\mathrm{g}\) for acceleration and \(\pm 2000\,^{\circ}/\mathrm{s}\) sensor range and a sensitivity of \(\,16\,^{\circ}/\mathrm{s}\). Through correlation analysis, we discovered that the acceleration data of the IMU provides better and faster data than the camera tracking system. Consequently, we used the internal IMU as data source for the acceleration parameters of the energy model. We conduct experimental flights hovering manually. Vertical movement was estimated using vertical flights changing height \(z\) within our laboratory (0.2 m-2.7 m). To estimate the horizontal movement, we conducted flights along the \(x\) and \(y\) coordinate (\(\pm 2\) m and \(\pm 1.5\) m respectively) within our laboratory. These experiments provide us with the necessary data to estimate the coefficients of the third order multivariate polynomials using regression. We use different sets of training data to generate four differently trained models:  
**Hover Model (Hover)** uses only the hovering movement without any movement in the \(x,y\) or \(z\) coordinates as training data. **Vertical Model (Vert)** uses in addition to the data used for the _Hover Model_ data of movements in mainly \(z\) direction. **Horizontal Model (Horiz)** uses in addition to the data used for the _Hover Model_ data of movements mainly in \(x,y\) direction. **Combined Model (Comb)** uses all available movement data from the training flights. These different training sets allow us to estimate the effect of the completeness of the training data towards the precision of the model.