## 3 Results and Discussion
### Experimental Results and Discussion
In other words, the order of visits between \(p_{6}\) and \(p_{3}\) has been switched, since \(p_{3}\) is now in the shortest path towards \(p_{6}\), and the robot can exploit this situation. The trajectories obtained using the proposed approach in the considered real-world scenario have been evaluated with respect to two aspects: (i) the ability to produce robot trajectories that allow for the collection of a meaningful sequence of measurements for subsequent tasks (i.e., the _quality of the measuring sub-trajectories_), (ii) the effects of the proposed trajectories on the battery levels and autonomy of the robot (i.e., the _quality of the charging sub-trajectories_). **Quality of the measuring sub-trajectories**Table 2 reports, for each PoI, the number of times it has been visited during the identified trajectories (# Visits) together with the prediction accuracy (in terms of MAPE, MAE, and MSE, respectively) of the Deep Learning (DL) model [15] trained to estimate future temperature values in this PoI. We compare the prediction accuracy of the DL model when it is trained with three different kinds of historical data: (i) the ones collected through the trajectories determined using the approach proposed in this paper, (ii) the ones collected using our previous approach [18], which does not consider charging requirements and allows only to skip some Pols inside direct trajectories, and (iii) the ones collected by always performing complete trajectories, as suggested in [17] (baseline). The results in Table 2 show that the proposed approach outperforms both previous methods since it allows for the production of a training set with which the estimation model in [15] can achieve a better accuracy (i.e., lower error). This holds both in the case of a single model for each PoI and of a unique model trained with the data of all PoIs altogether. Another aspect to notice is that this increased accuracy is connected with a greater number of visits to each single PoI in the same amount of time. This effect was also visible in [18], where the time to complete the fixed trajectory could be reduced by skipping the measurement activity in some PoIs, but in the current approach, it becomes more evident due to the robot's increased flexibility to move through each PoI. As a result,  
Figure 9: Application of the approach in: (**a**) Original environment. (**b**) Changed environment configuration. within a short time, the robot could collect more measurements at important Pols by visiting them multiple times. **Quality of the charging sub-trajectories**--As a final experiment, we evaluate the robot's battery level during the suggested trajectories. First, we performed tests to examine the impact of reducing the initial battery capacity on the number of times the robot had to stop for charging activities. The results showed that when the robot's full charge was set to 300 units, it made 257 stops at charging stations during all the considered 1656 trajectories. However, when the full charge was reduced to 150 units, the number of stops increased significantly to 406. Secondly, considering an initial battery level of 300 and the trajectories produced by the method in [18], we examined how many times the robot is located at Pols equipped with a charging station during a critical moment (i.e., its battery level is less than the threshold) and so can solve this situation without running out of charge in the middle of a trajectory. Our experiment shows that, with the previous approach, in approximately 168 cases, the robot needed to be charged, whereas in 83 of these cases, it was not located at Pols containing a charging station.