# Adaptive Path Planning for UAV-Based Pollution Sampling
## 4 Experiments and Results
### Results of Pollution Sampling with Adaptive Path Planning
#### 4.6.2 Black Carbon Measurement over Kongsvegen
\begin{table}
\begin{tabular}{l l} \hline \hline
**Scenario ID** & APPBlackCarbonKongsvegen \\ \hline
**Description** & Measurement of BC over Kongsvegen \\ \hline
**Stages** & 1. & The UAV begins its mission from the initial waypoint while airborne. \\  & 2. & The aircraft navigates to the area with the highest BC pollution. \\  & 3. & The aircraft measures pollution along a zigzag path. \\  & 4. & The global path is updated and the aircraft resumes the mission. \\  & 5. & The UAV finishes the measurements and returns to the airport. \\  & 6. & When communication fails, the UAV autonomously generates a new path to the goal locally. \\  & 7. & The UAV proceeds seamlessly to the goal waypoint. \\ \hline
**Map** & Rectangle area from (78.75000\({}^{\circ}\) N, 11.80000\({}^{\circ}\) E) to (78.94000\({}^{\circ}\) N, 13.20000\({}^{\circ}\) E), local origin \((0,0,0)\) at the Ny-Alesund Airport (78.92778\({}^{\circ}\) N, 11.87472\({}^{\circ}\) E, 0 m AMSL), 12h wind forecast \\ \hline
**Algorithms** & ACO\({}_{\mathbb{R}}\) (GPP), RRT* (LPP) \\ \hline
**Mission** & Start waypoint = \((3850,4800,5300,\frac{\pi}{4})\) \\
**parameters** & Goal waypoint = (22,750, 6850, 5500, \(-\frac{3}{4}\pi\)) \\  & Safety margin = 100 m (vertical), 200 m (horizontal) \\  & Airspeed = 22 \(\frac{\text{m}}{\text{s}}\) \\  & Flight-path angle limits (ascent, descend) = \(\left\langle-\frac{\pi}{90},\frac{\pi}{12}\right\rangle\) rad \\  & Max roll angle (symmetrical) = \(\frac{\pi}{30}\) rad \\  & Safety margin = 100 m vertically, 500 m horizontally \\ \hline
**Pollution model** & Linear cost growth from 0 at the center with rate = 0.005 m\({}^{-1}\) \\  & Initial center at (78.83420\({}^{\circ}\) N, 12.68260\({}^{\circ}\) E, 5000 m AMSL) \\  & Updated center at (78.83420\({}^{\circ}\) N, 12.68260\({}^{\circ}\) E, 5000 m AMSL) \\ \hline
**Criteria** & max(CT) \(\leq\) 4.55 s (for LPP) \\
**of success** & FT \(\leq\) 26,348 s (full case), FT \(\leq\) 21,125 s (simple case) \\  & NCOL = 0 (GPP and LPP) \\ \hline \hline \end{tabular}
\end{table} 
Table 10: BC measurement over Kongsvegen—mission outline. Figure 7: Measurement mission over the Kongsvegen glacier. As in the previous scenario, a simple static case was established to facilitate comparison with a human-designed path before attempting the full mission as outlined in Table 10. This reduced version included only steps 1, 2, 3, and 7, focusing solely on executing the initial measurement plan without any additional complications. Figure 8 illustrates the comparison: the top image presents the human-created reference path, while the bottom image displays the best (lowest COST) GPP-generated path. Here, the thin yellow line indicates the planned (theoretical) path, and the thick red line represents the actual simulated path. The manually designed path consists of only a few main waypoints, with the exact path between them managed by the simulated onboard low-level controller. As a result, the reference path is not verified for feasibility. In contrast, the path produced by the GPP includes densely spaced waypoints to follow the optimized path's shape closely and effectively navigate around static obstacles. Table 11 provides a summary of detailed metrics. All paths met the success criteria, although each generated path is longer than the reference path. Notably, paths 7, 11, 26, and 27 show EEE increase of over 10%, while Path 25 (compared to the reference path) reduces energy consumption by roughly 5%. CT varies from 19.359 s (Path 20) to 70.899 s (Path 5), with a median of 28.119 s. FT closely tracks EEE (with a correlation coefficient of 0.944), reaching a maximum value of 20,360.692 s for Path 26. No generated path resulted in any collisions (NCOL = 0). In contrast, the simulated reference-based path failed to reach the global endpoint, ultimately resulting in a crash (as shown in the top left close-up of Figure 8). However, in a real-world situation, a human operator would likely assume control of the UAV once it came within range, thereby avoiding the crash.