## 4 Results and Discussion
In the results presented in this paper, the authors focus on near-optimal solutions, setting the MCTS exploration parameter to 1.5 and the number of samples before the decision to 100. To lower the number of samples, it is possible to impose flight rules suggesting preferential algorithm-solving strategies. For the example in this study, the engineers may suggest the algorithm preferential bounds on when to recharge or communicate. After testing the feasibility of the scenario and obtaining a suboptimal solution without any alteration, the engineers can tweak some boundaries so that the MCTS follows a preferential branch. This interaction may provide further insights and test cases on the performances of the system in its operative environment. In the results of this paper, the system is biased toward communicating data when the storage of its flash memory is equal to or above 60% of its maximum. The system is also biased toward recharging when one of its batteries is below 20% of its charging state. These values were set after two algorithm runs, to help optimise the results without sacrificing a quick run time. The overall run time is relatively low, taking only a handful of minutes to assess the feasibility of a scenario. At the end of the simulation, the designers will be able to visualize a plan and the variation of the system resources during the simulated scenario. An example of the results that this analysis can provide is shown from Figures 9-12. The visualization of the plan in Figure 9 is heavily inspired by the tools routinely used in mission control centres. It is an intuitive view, defined as a function of mission time. This view can provide an initial operational timeline to be discussed in round tables with the stakeholders or refined during the different design phases. It is interesting to pinpoint that this operational timeline is \begin{table}
\begin{tabular}{c c} \hline \hline
**Variable** & **Described by/Derived from** \\ \hline Waypoints to visit & Analysed test scenario \\ \hline Last action & Functional analysis and ConOps definition \\ \hline Current waypoint & Analysed test scenario \\ \hline Battery capacity & Rover sizing rules and rover resources template \\ \hline Sample storage capacity & Rover sizing rules and rover resources template \\ \hline Data volume (In the system memory) & rover sizing rules and rover resources template \\ \hline Number of recharges & Terminal variable \\ \hline Number of communication & Terminal variable \\ \hline Idle time & Terminal variable \\ \hline Mission time & Analysed test scenario \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Set of variables that define a state of the rover. directly generated from the identified set of system functionalities. Hence, timelines with different granularity can be generated while detailing the functional architecture. Figure 10 shows the two batteries' (lower and upper) consumptions clearly. The performances agree with what was recorded from IGLUNA and the specification of the Leo rover [62]. The battery connected to mobility has a capacity of around five hours. In comparison, the upper battery has more capacity but a higher power consumption. For example, during IGLUNA the upper battery was the one depleting the fastest due to the high power requirements of navigation equipment such as LiDAR and the cameras. Figure 11 shows the data generated. The system needs to relay data multiple times not to surpass the provided limit of 2 Gbytes. The rover has a sampling storage set at 1 g, while a sample weighs around 0.2 g. The system can only sample some of the waypoints, trying to maximize its sampling-related reward set as \(R_{sampling_{raw}}=[8,4,10,9,10,4,6,8,4,4]\). The optimal path evaluated with the routing algorithm provides a reference for the optimal navigation actions. These optimal actions are suggested to the algorithm. Figure 12 shows the path followed by the simulated system. The green points delimit areas with rugged terrain or obstacles not-traversable for the rover.