## 5 Conclusions  
This work presents a novel IPP algorithm based on GA theory, renamed GPP, to address the path planning problem in underwater environments. The strategy relies on the concepts of genetic operations, namely crossover and mutation, to generate the optimal path in terms of informativeness and coverage of the inspected area. The proposed approach has been tested offline, exploiting real marine data, within the context of PO monitoring, where the GPP aims at generating an optimal path for an AUV to investigate the presence of PO in a target area. To this aim, the GPP algorithm exploits the uncertainty of a GP prediction, which is employed to provide an a priori knowledge about the presence of PO in the area of interest. The algorithm uses such uncertainty to compute the DE in the points belonging to each generated path, which is adopted to represent their information content. Afterwards, the GPP method retrieves the path that maximises an utility function, consisting of an averaged version of the DE along the path and a geometric index representing the coverage of the inspected area. A comparative study with other two IPP strategies has been carried out to evaluate the performance of the proposed GPP method. The first one is a S-B algorithm, which is considered, to the extent of the authors' knowledge, as one of the main state of the art IPP approaches for IG operations. The second strategy is instead a Random planner, which has been taken into account to assess whether the genetic iterative operations performed in the GPP algorithm provide improvements to the path performance, or they can be avoided to reduce planning time. Thus, this planner implements only the first phase of the GPP method, generating random chromosomes for all the available planning time, without applying any crossover or mutation transformations, and in the end the best chromosome in terms of utility value among all the generated ones is selected. The comparative analysis has been performed over 3 different areas, located in Mallorca Island (Spain), and considering 3 starting points for each area. The result of the tests showed that the GPP generally performs better than the S-B planner in terms of all the metrics used for the comparison: ME, PL, and O2E. The performance of GPP and Random planner result similar in terms of PL and O2E, but the GPP usually generates more informative paths (higher ME). As a future work, the issue of retraining the GP exploiting the data acquired by the AUV along the planned path will be addressed. This will allow the AUV to update the model of the environment and to replan its path according to the newly acquired information. Moreover, alternative methods to perform the consistency check on the genes of the chromosome will be investigated, e.g., a cost factor in the utility function could be introduced to penalise those genes which are outside \(X_{free}\). Furthermore, strategies to avoid the issue of overlapping path sections will be studied. Indeed, the presence of loops within a path implies that the same point will be visited multiple times. Therefore, the information acquired by the AUV along such path will be conditioned by the redundant points, which will contribute more than once to the global information content of the path. This issue cannot be addressed by updating the GP model every time a new data is available, due to the computational cost of this operation. However, the problem could be solved imposing some penalties in the utility function when a path has overlapping sections. Finally, the proposed GPP algorithm will be implemented on an AUV and tested on field experiments to assess its performance in a real marine scenario. Conceptualization, A.T., E.G.-F., F.B.-F. and R.C. ; methodology, M.B., F.R., S.T., G.P., A.T., E.G.-F. and R.C. ; software, M.B., A.T. and E.G.-F.; formal analysis, M.B. ; investigation, M.B., G.P. and A.T.; data curation, E.G.-F.; writing--original draft preparation, M.B., F.R., S.T., G.P.