## 6 Baseline Statement and AAMO Approach Results
### Aamo Assessment
#### 6.4.4 Aamo Efficiency Assessment  
In this section, several statistical analyses were performed on different indicators to demonstrate the efficiency of the proposed _AAMO_ approach. Wilcoxon signed-rank tests were performed (a non-parametric test was chosen since data in each condition do not follow a normal distribution) to compare samples from two populations. More precisely, it tests the indicator differences between approaches for a given fleet size. Firstly, in relation to TT (see Figure 23), the evidence confirms two expected results: (i) All approaches benefit from adding robots to the fleet. A Wilcoxon difference test was performed regarding TT and the fleet size for each approach. All comparisons present a significant decrease in TT when fleet size increases (\(p\)-value \(<0.05\)); (ii) Since it only takes care of connectivity, the EbC approach shows the worst performance regardless the fleet size. Wilcoxon tests showed a significant result (\(p\)-value \(<0.001\)) for all comparisons between approaches given a fleet size. Additionally, all _AAMO_ instances show competitive TT results even slightly outperforming other approaches in the case of _AAMO:10_. In particular, a Wilcoxon difference test showed that _AAMO:10_ has a smaller TT than _MinPos_ for 2 and 5 robots (resp. \(W=169\), \(p\)-value \(<0.01\), and \(W=159\), \(p\)-value \(<0.05\)). Secondly, concerning the PL indicator (see Figure 24), the EbC approach present the worst performance, coherently. Again, the Wilcoxon test showed significant results (\(p\)-value \(<0.001\)). Likewise, all _AAMO_ instances show competitive results too. Besides, and as was pointed above, the TT and PL results show that the lack of ideal communication conditions negatively affects the _MinPos_ approach more than the _Yamauchi_ approach. Wilcoxon tests showed a trend for 4 and 5 robots (\(p\)-value \(<0.1\)) concerning TT, and a sigfinicant difference in PL for 4 robots (\(p\)-value \(<0.05\)). Up to this point, the _AAMO_ approach has shown results as good as the _MinPos_ approach. Next, the indicators related to connectivity are analysed in order to properly assess the potential advantages of the _AAMO_ approach in the presence of more realistic communication conditions. The DLR indicator trend is shown in Figure 25. As can be seen, while the performance of the _MinPos_ and _Yamauchi_ approaches are the worst, the EbC performance is remarkably the best. These visual results were confirmed by Wilcoxon tests between approaches for each fleet size. DLR indicator is significantly bigger (\(p\)-value \(<0.001\)) for MinPos and Yamauchi than AAMO and EbC approaches, except for 8-sized fleets where these results are significant only when compared to EbC. Moreover EbC has a significant smaller DLR indicator (\(p\)-value \(<0.05\)) than all the others approaches except for the 5 and 8 robots cases, in which any statistical difference can be found between AAMO approaches and EbC. Figure 23: Total Exploration Time (TT) under non-ideal communication conditions. Similarly, the _AAMO_ approach results represent a very good improvement with respect to both _MinPos_ and _Yamauchi_ approaches. The chart in Figure 25 reveals that our approach outperforms both Yamauchi and minPos approaches independently of the fleet size on average. Nevertheless, the smaller fleet, the greater outperforming. The explanation can arise correctly from intuition: when the environment is bounded, the probability of being disconnected tends to decrease as the fleet size increase. Therefore, the benefits of our approach tend to be smaller when the fleet size increases. Either way, it is always meaningful. Please note that even in the largest fleet size case, the DLR of _AAMO_ represents an improvement of 20% on average compared to the corresponding _Yamauchi_ or _MinPos_. Furthermore, the relation between TT, DLR and _HO-Threshold_ is noticeable. The more effort demanded by the human operator (higher threshold), the slower but higher connected the _AAMO_ performs.