## 6 Baseline Statement and AAMO Approach Results
### Baseline Statement
#### 6.3.2 Baseline Assessment  
We start the analysis highlighting that both approaches can adequately explore all the environments presented above in Section 6.1.1. Coherently, both approaches achieve high levels of _CR_. This can be seen clearer in Figure 12.  
Furthermore, the _minPos_ approach outperforms _Yamauchi_ concerning TT as was expected. However, the most notorious differences of performance are observed on fleets which size is less than or equal to five robots, as can be seen in Figure 13. \begin{table}
\begin{tabular}{l l l l l l l l l l l l l l l l} \hline \hline \multirow{2}{*}{**FM**} & \multicolumn{6}{c}{\(|\mathbf{R}|\)} \\ \cline{2-13}  & \multicolumn{3}{c}{**1**} & \multicolumn{3}{c}{**2**} & \multicolumn{3}{c}{**3**} & \multicolumn{3}{c}{**4**} & \multicolumn{3}{c}{**5**} & \multicolumn{3}{c}{**8**} & \multicolumn{3}{c}{**10**} \\ \cline{2-13}  & \multicolumn{3}{c}{**AVE**} & \multicolumn{1}{c}{**StD**} & \multicolumn{1}{c}{**AVE**} & \multicolumn{1}{c}{**StD**} & \multicolumn{1}{c}{**AVE**} & \multicolumn{1}{c}{**StD**} & \multicolumn{1}{c}{**AVE**} & \multicolumn{1}{c}{**StD**} & \multicolumn{1}{c}{**AVE**} & \multicolumn{1}{c}{**StD**} & \multicolumn{1}{c}{**AVE**} & \multicolumn{1}{c}{**StD**} & \multicolumn{1}{c}{**AVE**} & \multicolumn{1}{c}{**StD**} \\ \hline \multirow{2}{*}{TT} & Y & 1958 & 121.8 & 1288 & 255.9 & 902 & 128.6 & 791 & 125.6 & 647 & 78.5 & 516 & 41.3 & **459.5** & 41.5 \\  & MP & **1898** & 148.0 & **1044** & 110.1 & **779** & 72.8 & **615** & 52.0 & **505** & 48.8 & **496** & 19.7 & 482 & 37.4 \\ \hline \multirow{2}{*}{PL} & Y & 1308 & 94.0 & 1581 & 157.4 & 1665 & 158.9 & 1831 & 225.7 & 1896 & 186.2 & 2093 & 95.9 & **2294** & 173.9 \\  & MP & **1268** & 59.0 & **1413** & 124.1 & **1420** & 85.4 & **1467** & 94.8 & **1592** & 107.8 & **2053** & 50.3 & 2438 & 140.7 \\ \hline \multirow{2}{*}{CR} & Y & 99.1 & 0.09 & 99.0 & 0.06 & 99.0 & 0.04 & 99.1 & 0.05 & 99.0 & 0.04 & 99.0 & 0.05 & 99.1 & 0.04 \\  & MP & 99.0 & 0.04 & 99.1 & 0.06 & 99.1 & 0.06 & 99.1 & 0.05 & 99.1 & 0.05 & 99.1 & 0.07 & 99.1 & 0.07 \\ \hline \multirow{2}{*}{OSR} & Y & 0.0 & 0.00 & 1.23 & 0.57 & 2.22 & 0.48 & 3.68 & 0.70 & 5.25 & 0.81 & 5.45 & 0.21 & 7.00 & 0.12 \\  & MP & 0.0 & 0.00 & **0.93** & 0.19 & **2.31** & 0.38 & **2.94** & 0.25 & **4.37** & 0.50 & 5.45 & 0.11 & 7.00 & 0.08 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Yamauchi and MinPos results under ideal communication conditions on Maze environment. Figure 12: Coverage ratio (CR) under ideal communication conditions. Both approaches achieve a coverage bigger than 99% of the terrain regardless of the fleet size. In crowded environments, going from one location to another is often more difficult than in the presence of fewer robots. Therefore, due to collision avoidance manoeuvres, both approaches show an increasing _PL_ when the fleet size increases. This behaviour may be observed in the corresponding chart in Figure 14. On the one hand, _Yamauchi_ presents a trend with an almost invariant slope along the different fleet size values. On the other hand, under _MinPos_, the trend of _PL_ presents a positive but minor slope from one to five-robot-sized fleet after what it becomes very steep. Hence, the analysis is divided into two cases. Firstly, when fleet size is less than or equal to five robots, _MinPos_ is more efficient than _Yamauchi_ since both approaches achieve very similar coverage ratios (see Figure 12) despite in the latter robots need to traverse longer distances than in the former, on average. That is expected since the _Yamauchi_ approach does not take care about the dispersion of the fleet as the _MinPos_ does and consequently, in the former robots are forced to deal with crowding more frequently than in the latter. This is a remarkable difference given that the energy needed to support an exploration mission will be closely related to the distance traversed by robots.