# Adaptive Path Planning for UAV-Based Pollution Sampling
## 5 Discussion
It could compute a globally optimal solution, which requires more time, or quickly find a feasible but suboptimal local path. Computing local paths separately also addresses the issue of mostly unpredictable wind gusts, which can invalidate a path that is too strict. The APP produces collision-free paths that are optimized to reduce the UAV's energy usage, achieving quality comparable to human-designed paths. It shows optimal performance for shorter paths with minimal altitude changes and calm wind. However, as the wind speed nears the UAV's flight speed and the path length grows, the paths generated by the APP may be less effective than those crafted by human planners. This is due to discrepancies in path shape between optimization and simulation, caused by a simplified model of wind's impact on energy usage. In the optimization phase, wind impacts ground speed \(v_{g}\) but does not adjust the UAV's direction, leaving the path shape unaffected. Adopting wind-enabled Dubins paths, as explored in [PERSON] et al. [51], could address this issue. Therefore, for longer paths affected by strong winds and large altitude variations, the results are satisfactory, although they do not achieve the quality of those paths designed by humans yet. The LPP rapidly computes new kinematically feasible and obstacle-free local paths, which are calculated dynamically and adapt to a changing environment. Nevertheless, due to the random nature of RRT algorithms, they lack reproducibility and are rarely energy-optimal. By increasing the number of iterations, RRT* can approach the optimal solution but at the cost of significantly increased computation time. It should be noted that the software evolved during the research, so different tests incorporated varying releases of the APP. For example, the comparison tests used the first release of the APP described in [30], while the HIL tests incorporated its updated version. The final integration experiments shown in Figure 11 used the latest APP. While the research focused on Model-in-the-Loop verification, it also included some Hardware-in-the-Loop validation on a small fixed-wing UAV. The next steps will include extending the work to Software-in-the-Loop and Processor-in-the-Loop verifications. Future research will also consider several compound Hardware-in-the-Loop validation tests once the final HALE UAV's physical model is completed. The APP will be further developed to address the Zermelo problem (i.e., incorporating wind) instead of using Dubins paths. To achieve this, a model incorporating wind effects will be implemented. The 2D model by [PERSON] et al. [51], for example, could be used as a base and subsequently extended to 3D. Further enhancing the wind model to include vertical air currents would enable the APP to incorporate more advanced techniques of energy-efficient flight, like soaring [52] and pursuing warm air masses. For a more reliable assessment of the APP's performance, it would be beneficial to expand the reference set to include a broader range of mission scenarios and feedback from multiple human experts. This will be a focus of future research. This research does not yet include a multi-objective optimization approach. Future work will explore this by comparing the single-objective method with alternative multi-objective optimization methods, such as a multi-objective GA. Moreover, this study lacks a comprehensive comparison to other similar solutions, which will also be addressed in subsequent publications.