# Adaptive Path Planning for UAV-Based Pollution Sampling
## 4 Experiments and Results
### Results of Pollution Sampling with Adaptive Path Planning
#### 4.6.3 Final Modifications and Results  
Many APP components, especially the environment model and GPP, have been modified significantly during the study. A working but not final implementation is described in detail in [30]. However, several changes have been introduced during the final stages of the study. In the final version of the environment map, pollution modeling was changed from estimated intensity dispersion map to a set of predefined parametric measurement maneuvers. Now, an expert chooses the placement, type, and parameters of the measurement maneuvers to perform, and the GPP generates the optimized path to reach them and fly back. \begin{table}
\begin{tabular}{r r r r r r r r r} \hline \hline
**\#** & **CT [s]** & **FT [s]** & **CFEs** & **COST** & **LEN [m]** & **SMOO** & **EEE [Wh]** & **NCOL** \\ \hline
**1** & 66.197 & 17,426.153 & 13,850 & 525 & 265,305 & 0.771 & 506.069 & 0 \\ ⋮����������������������������������������������������������  
Additionally, the airspace map was changed to include enforcer zones, which act opposite to obstacles. All waypoints of the path must be placed inside the enforcer zones for the path to be feasible. The final version of the GPP computes path segments between multiple measurement sites, i.e., from the end of one measurement path to the beginning of the next one. In the previous release, as in [30], the GPP generated only two segments: (1) from the start waypoint to the start of the measurement path and (2) from the end of the measurement path to the end waypoint. Previous implementations of the GPP used densely sampled Dubins airplane paths. This is ineffective in the case of a real non-deterministic environment, especially in the presence of strong variable wind. That is because the controller tries to adhere to the theoretical path as closely as possible. Contrary to that, in some cases, it is better to use a more reactive approach. Reducing sampling density provides more room for the controller to adjust. Thus, it reduces the number of small-scale maneuvers completed against wind. This issue was mentioned in [30]. Therefore, the newest implementation of the GPP favors predefined maneuvers available in ArduPilot to simplify the final path. Path density is also adjusted to the maneuver type, so straight lines are defined only by the farthest waypoints, while curve-like maneuvers have several waypoints. Moreover, the GPP has been integrated with ArduPilot Mission Planner [50], which acts as a human-GPP and human-UAV interface. It enables conveniently planning the initial mission plan and to transfer it to GPP, and then to verify the optimized plan and send it to the UAV. The first integration test considered simulated pollution profiling over Gliwice Airport (Poland). The UAV must fly from starting point over the airport to the point of the highest predicted pollutant concentration located nearby, perform horizontal zigzag profiling over the area, and fly back to the airport. This is the basic scenario, which will be used for testing the real UAV. In this scenario, wind is uniform on the horizontal plane and vertically defined using manually acquired data from free wind servers. Figure 9 depicts the path optimized by the GPP and loaded into Mission Planner. Blue markers and thin orange lines mark the map boundaries. Green markers are global waypoints, and thick yellow lines with arrows represent the global path returned by GPP. Figure 9: Optimized path as presented in Mission Planner—initial experiments. The path verified in simulation was sent to a real small fixed-wing UAV and validated in real-life operating environment. The real path and flight altitude are presented in Figure 10.  
The final integration test considered a similar scenario of simulated pollution profiling over Gliwice Airport. The measurement strategy, however, is defined differently, using new parametric maneuver-based convention discussed above. The initial mission configuration in Mission Planner is presented in Figure 11.  
Now, map boundary is defined by a more complex polygon.