## 6 Baseline Statement and AAMO Approach Results
### Aamo Assessment
#### 6.4.4 Aamo Efficiency Assessment
way to favour the efficiency; (ii) In the presence of damages or inner failures the exploration strategy should take those events into account preventing the need of re-exploration. In Figure 27 the trend followed by Maximum Disconnection Last Ratio MDLR indicator is depicted showing that the bigger _HO-Threshold_, the shorter disconnection periods (Wilcoxon tests showed that the MDLR indicator is significant smaller (\(p\)-value \(<0.05\)) for AAMO:20 than for AAMO:10, for 2 and 3 robots, and tends to be smaller (\(p\)-value \(=0.09\)) for 4 robots) and that the last of isolation situations is at most equivalent to half of the DLR values for every fleet size and _HO-Threshold_ value as well. In other words, the isolation situations regard more than one single robot and this in turn, reveals that under the _AAMO_ approach the robots often intent to rejoin each other. At last but not least, it is worth to discuss the trend of OSR as the fleet size increase. The results obtained by the different _AAMO_ instances are depicted in Figure 28. In Section 6.3 the OSR levels were achieved mostly thanks to simultaneous sensing actions, conversely, in these simulation runnings, the OSRs achieve higher levels due to non-ideal communication conditions. As was expected, the more the mapping information of the robots is out-of-date with respect to each other, the higher the OSR. However, in any communication conditions, the same upper bound is achieved. This suggests that the size and bounded condition of the _Maze_ environment could be limiting the over-sensing phenomenon when fleet size increase beyond five robots. To sum up and concerning the _Maze_ scenario and the baseline stated in Section 6.3, the conclusions of this section are: (i) The _AAMO_ approach can be employed as a strategy to coordinate multi-robot systems that are dedicated to exploration tasks; (ii) As was expected, the _HO-Threshold_ value directly impacts on the connectivity level that the fleet is able to hold during the mission; (iii) Likewise, the relation between _HO-Threshold_ values and the TT and DLR/MDLR indicators is the expected: the bigger the _HO-Threshold_ value, the worse TT performance, but the better DLR/MDLR ratios; (iv) Although all instances of the _AAMO_ approach present TT degradation with respect to the baseline, in any case it is not significantly due to the computation of the proposed task-to-robots distribution; (v) All _AAMO_ instances outperform the baseline concerning the DLR and MDLR indicators; (vi) With the exception of DLR/MDLR, all instances of the _AAMO_ approach outperform the _Ebc_ approach; (vii) The topology of the fleet networks shown during exploration is consistent with the _HO-Threshold_ values, for all _AAMO_ instances. Figure 27: Maximum Disconnection Last Ratio (MDLR) under non-ideal communication conditions. MDLR shows the longest individual isolation period registered by some fleet member along the exploration. The trend is oscillatory following the same pattern as the DLR indicator. The _AAMO_ approach shows effectiveness and flexibility (through the _HO-Threshold_ setup) to tackle the multi-robot exploration problem. Particularly concerning the efficiency related to both completion time and connectivity level maintenance, the approach appears as an intermediate solution that presents much better TT performance than the most restrictive approach _EbC_ and better connectivity level along exploration than the approaches that do not take care about communication issues.