## 6 Baseline Statement and AAMO Approach Results
### Aamo Assessment
#### 6.4.4 Aamo Efficiency Assessment
This claim is confirmed by Wilcoxon tests that showed a significantly bigger (\(p\)-value \(<\) 0.05) TT indicator for AAMO:20 than for AAMO:10, and also show that the DRL indicator is significantly smaller (\(p\)-value \(<\) 0.05) for AAMO:20 than for AAMO:10, regardless the fleet size. Regarding the oscillation registered, it could suggest the existence of the following rational pattern. When fleet size is even, the easier way to avoid isolation situations is keeping in pairs (connected with at least another teammate). Contrarily, when the fleet size is odd, not all robots can keep in pairs. In case the fleet has divided, at least one sub group must be composed of three robots. Therefore, this  
Figure 24: Path length (PL) under non-ideal communication conditions. Figure 25: Disconnection Last Ratio (DLR) under non-ideal communication conditions. The bigger the _HO-Threshold_, the smaller DLR. This fact holds showing an oscillatory behaviour as the fleet size increase. oscillatory behaviour could hint at the fact that odd-sized fleets need to make little more effort to avoid robot isolation situations and are consequently subject to bigger DLR results as well. Likewise, it is interesting to analyse the DLR indicator and network topology together. This way it is possible to get a closer notion about the interaction between robots along the exploration. Figure 26 is devoted to showing the number of connected components present in the network, averaged over time. Please note that for the AAMO:20 instance--run on 2-Robot fleet--the DLR is about 40% (see Figure 25), coinciding with the percentage achieved by the 2 CC of the same fleet size in Figure 26. In other words, the fleet holds a network composed of one single connected component during 60% (100%-40%) of total exploration time. Consistently, this is equivalent to say that during this portion of the time none robot has been disconnected. Additionally, and as a matter of fact, the chart shows that as the fleet size increase it is more challenging to keep the whole fleet connected: 1 CC stack is decreasing in size as the fleet size increase. Nevertheless, it also shows that simultaneously with the adding of new robots, the fleet is more and more cohesive (in relative terms). This fact may be corroborated looking at the upper part of the chart where the stacks corresponding to the greatest number of connected components are plotted. The following pattern can be observed: the number of connected components (given by \(nCC\)) increase slower than the fleet size \(n\). Again, the fact that the _Maze_ scenario is bounded may explain this phenomenon to a large extent. Although all this information gives an approximated notion about how disconnected is the fleet (group perspective) along explorations, it is not enough to hint what is happening at the individual level. Thus, it is also interesting to study the worst case of the individual disconnections last. This way it is easier to evaluate both coordination capabilities (how long a robot is unable to coordinate its actions with any other teammates) and risky situations (how long the fleet present single points of failure). Recall that the key motivations in considering communication constraints are strongly related with the rework avoidance: (i) When robots are unconnected they have fewer possibilities to coordinate their actions hence they could visit the same regions unnecessarily. Hence, keep them connected is a  
Figure 26: Network topology composition under non-ideal communication conditions averaged over time. Depending on the number of connected components and the fleet size, it is possible to study the existence of sufficient conditions to fall into isolation situations. For instance, for a 3-Robot fleet, the 2 CC or 3 CC topologies imply having at least one robot isolated while for a 5-Robot fleet this implication is related to 3 CC, 4 CC, or 5 CC topologies, and so on.