# Adaptive Path Planning for UAV-Based Pollution Sampling
## 5 Discussion  
The GPP underwent comprehensive testing, beginning with an evaluation of the effects of the individual optimization criteria, followed by a performance comparison across various optimization algorithms. As a result, the criteria successfully adjusted the generated path according to the definitions outlined in Section 4.3. The energy estimation criterion considers the impact of wind speed on flight time indirectly but does not account for the path's shape. Consequently, this criterion loses reliability when the wind speed approaches the UAV's airspeed. The minimal path length and energy expenditure criteria yield similar results, so using both together is unnecessary. However, each has distinct advantages: the energy-based criterion is more precise and is preferable when computation time is not a constraint, while the path length criterion is a better choice when quick calculations are required. The precision of the current energy expenditure model is somewhat questionable. While it considers factors like altitude, airspeed, and angle of attack in calculating propeller power, it neglects the effects of other maneuvers, such as turns. This limitation is especially evident during yawing, caused by thrust differences between the twin propellers in a TS configuration. Adopting a more accurate model could substantially alter the GPP's results; for example, it might prioritize ascending on long, zigzag paths over helices to reduce the energy demands associated with frequent turning. Disabling the criterion of pollution intensity enables the APP to be used for planning energy-efficient point-to-point paths, making the algorithm versatile for applications outside its main purpose. The analysis of the optimization algorithms revealed ACO\({}_{\mathbb{R}}\) and PSO as strong candidates. ACO\({}_{\mathbb{R}}\) excels in path quality, while PSO achieves high marks for both median values and standard deviations. The ACO\({}_{\mathbb{R}}\)-based GPP generates paths similar to those created by a human expert. In the first use case, many of the GPP-produced paths were marginally shorter, smoother, and more energy-efficient. Conversely, in the second case, the results  
Figure 12: Final simulated path. were generally acceptable but slightly fell short of the quality of the human-designed path. Interestingly, when the focus of optimization shifts to standard deviations (when they are assigned greater importance than medians), the GA becomes the leading choice. The GA demonstrates greater robustness and produces more stable paths across consecutive runs compared to the other algorithms. The choice regarding the optimal algorithm depends on the specific objective at hand, enabling any of these options to be selected based on the desired outcome. In this research, the GPP is primarily designed to assist the human operator rather than to fully replace them. Paths are validated in simulations and require manual approval before being sent to the UAV. As a result, discarding a small fraction of suboptimal paths is acceptable and is justified by the improvement in path quality. Therefore, ACO\({}_{\text{R}}\) is the preferred algorithm for the GPP as a mission planning tool. However, if the GPP operates autonomously, the GA is the preferred option due to its robustness. PSO, with its versatility, also offers potential for future exploration. In contrast, I-GWO was generally outperformed by the other algorithms across most criteria. Dubins airplane paths remain optimal when wind effects are minimal. However, if the wind speed is near the UAV's airspeed, causing significant path distortion, a wind-adjusted model becomes essential. In windy conditions, the primary cause of suboptimality in APP-generated paths is the divergence between the actual flight path and the expected theoretical path anticipated by the controller. By using two-stage path planning and different algorithms for local and global planning, the APP can be fine-tuned to focus on different tasks.