## 3 Results
### Comparative Analysis  
A comparative offline analysis has been carried out to assess the performance of the three considered IPP strategies. For each scenario in each geographic area, 40 tests have been conducted, evaluating the metrics detailed in Section 2.3. The planning time has been set to 25 s for all the three planners, in which the duration of the initialisation phase for both GPP and S-B has been set to 10 s.  
Note that the algorithm proposed in [14] is executed for 15 s (5 of initialisation and 10 of optimisation). Here, both the phases have been extended not to risk to limit it in its performance, also in compliance with the underwater scenario that, in view of typically slowly dynamics with respect to a ground one, may allow longer planning time. Tables 2-4 report the results, in terms of the metrics mean and three times the standard deviation, for each planner in all the scenarios of Areas 1-3, respectively. The scenario name is referred to the starting point selected in the area of interest. \begin{table}
\begin{tabular}{c l c c c} \hline \hline  & \multicolumn{4}{c}{**AREA 1 (Figure 4a)**} \\ \hline
**Scenario** & **Planner** & **ME [bits]** & **PL [m]** & **O2E [m]** \\ \hline \multirow{3}{*}{First} & Genetic & \(0.58\pm 0.01\) & \(20.2\pm 3.39\) & \(14.3\pm 2.56\) \\  & Random & \(0.58\pm 0.01\) & \(20.2\pm 3.28\) & \(15.1\pm 5.20\) \\  & S-B & \(0.53\pm 0.02\) & \(18.1\pm 12.7\) & \(10.7\pm 1.29\) \\ \hline \multirow{3}{*}{Second} & Genetic & \(0.57\pm 0.01\) & \(20.6\pm 3.15\) & \(18.0\pm 3.31\) \\  & Random & \(0.57\pm 0.01\) & \(20.3\pm 3.38\) & \(17.6\pm 3.86\) \\  & S-B & \(0.55\pm 0.01\) & \(26.7\pm 6.74\) & \(19.3\pm 1.00\) \\ \hline \multirow{3}{*}{Third} & Genetic & \(0.54\pm 0.01\) & \(20.9\pm 2.68\) & \(17.0\pm 6.79\) \\  & Random & \(0.54\pm 0.01\) & \(20.6\pm 3.00\) & \(16.6\pm 4.80\) \\ \cline{1-1}  & S-B & \(0.52\pm 0.01\) & \(21.5\pm 13.5\) & \(12.4\pm 1.39\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Results, in terms of mean and three times the std. dev., of the 3 planners in 3 scenarios of Area 1. \begin{table}
\begin{tabular}{c l c c c} \hline \hline  & \multicolumn{4}{c}{**AREA 2 (Figure 4b)**} \\ \hline
**Scenario** & **Planner** & **ME [bits]** & **PL [m]** & **O2E [m]** \\ \hline \multirow{3}{*}{First} & Genetic & \(0.51\pm 0.02\) & \(31.7\pm 6.59\) & \(21.5\pm 20.9\) \\  & Random & \(0.50\pm 0.02\) & \(30.3\pm 5.07\) & \(13.1\pm 7.13\) \\  & S-B & \(0.48\pm 0.15\) & \(13.4\pm 25.1\) & \(8.27\pm 10.7\) \\ \hline \multirow{3}{*}{Second} & Genetic & \(0.51\pm 0.01\) & \(33.6\pm 5.89\) & \(27.7\pm 9.77\) \\  & Random & \(0.50\pm 0.01\) & \(32.0\pm 4.82\) & \(26.3\pm 11.5\) \\ \cline{1-1}  & S-B & \(0.50\pm 0.03\) & \(22.3\pm 19.8\) & \(17.7\pm 17.4\) \\ \hline \multirow{3}{*}{Third} & Genetic & \(0.51\pm 0.01\) & \(28.7\pm 5.85\) & \(14.2\pm 5.24\) \\ \cline{1-1}  & Random & \(0.50\pm 0.01\) & \(30.3\pm 5.08\) & \(13.1\pm 7.13\) \\ \cline{1-1}  & S-B & \(0.42\pm 0.12\) & \(26.5\pm 22.5\) & \(20.3\pm 16.0\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results, in terms of mean and three times the std. dev., of the 3 planners in 3 scenarios of Area 2. Analysing the percentage of times each planner performs better than the other ones in terms of ME only, the results are reported in Table 5. Figures 5-7 show a subset of the paths generated by each investigated planner in the three areas of inspection. In fact, although 40 optimal paths have been generated for each trial, just five of them have been randomly selected and represented, in order to maintain a good readability of the figures. Instead, Figures 8-10 display the plots of the ME, PL, and O2E values, respectively, over 40 trials for each planner in all the three geographic areas. For the sake of readability, only the plots of the first scenario for all the 3 areas of interest are reported. In fact, the other scenarios provide similar plots, without any anomalies in the results.