## 4 Discussion  
The results of the comparative study show that the proposed GPP generally performs better than the S-B and Random planners. In terms of ME, the GPP algorithm statistically outperforms the others for all the studied cases (area and scenario). It is worth discussing the case of Area 2-Scenario 1 and 2, in which the percentage of times the S-B planner performs better than the other approaches results to be greater than the other areas, as represented in Table 5 and Figure 8b. This is because the starting point of the first and second scenarios is located in an area characterised by a high variance of the GP. Therefore, the S-B planner is likely to find its _Station_ point close to the starting point, resulting in a short path with high information content. This situation could occur whenever the most informative point is located near the starting point, despite the amount of target area investigated by the _Station Search_ phase of the S-B planner. Consequently, the ME metric could result in a higher value for the S-B planner in similar scenarios, potentially leading to misleading considerations if not associated to the other two metrics. In fact, the standard deviation of the ME for the results of S-B planner in Area 2-Scenario 1 is much higher than the one obtained by GPP, as reported in Table 3 and Figure 8b, and that highlights a statistic inconsistency in the S-B planner results. On the contrary, the other two approaches generate paths with a higher mean value of ME and lower standard deviation over the 40 offline executions.  
Moreover, the proposed GPP algorithm results to generate paths with a good coverage of the area under inspection. In particular, as shown in Figures 9 and 10, the PL and O2E metrics obtained by the GPP strategy result to be significantly better than the ones of the S-B planner, allowing to explore a wider portion of the target area as well as gathering more useful data about PO along the path. The PL and O2E metrics result comparable between the GPP and the Random planner. This is reasonable since the two planners exploit the same distributions and parameters for the chromosome generation. However, as reported in Table 5, the ME of the GPP approach is statistically higher than that of the Random planner, thus justifying the use of the iterative part of the GPP algorithm. Furthermore, Figure 11 shows the improvement obtained thanks to the optimisation phase  
Figure 11: Utility value trend over the optimisation phase of the GPP algorithm. (**a**) Area 1; (**b**) Area 2; (**c**) Area 3.  
performed in the GPP algorithm, and not included in the Random planner instead. The mean trend of the utility value, represented in red in the plot, is strictly increasing but does not reach convergence in the defined planning time \(t_{max}\). Thus, the choice of utilising a stop condition based on time become more relevant, since the convergence of the algorithm could require an amount of time which will make the algorithm not exploitable online. An analogous result is reported also in [14], which shows the evolution of the utility value over an extended planning time of 180 s for the S-B planner. The reader is there addressed, as a S-B characterisation is beyond the scope of the authors' manuscript.  
Last, it is worth pointing out that the results obtained by the GPP algorithm, utilising the parameters configuration discussed in Section 3.2, have similar performance over the three different target areas, although obtained through optimisation on a specific area (Area 1). This confirms that the considered set of parameters has a validity independent from the specific area and scenario.