# Adaptive Path Planning for UAV-Based Pollution Sampling
## 4 Experiments and Results
### Results of Analysis of the Selected Optimization Methods
These were ranked lowest in priority as the GPP functions as a high-level planner without stringent timing constraints. To account for the variability in the results, it was decided to include standard deviations in the optimization process. However, since GPP outputs are initially validated through simulation and subsequently reviewed by a human operator, the optimization favored median values instead of standard deviations. The metric importance hierarchy remained unchanged, with weights assigned to standard deviations only after those for the medians had been determined. \begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline  & & & & & & & & \\ \hline Metric & CT [s] & CFEs & COST & LEN [m] & SMOO & NPTS & EEE [kWh] & NCOL \\ Mean & 252.596 & 12,740 & 531.806 & 132,764 & 0.754 & 1331 & 1.171 & 0 \\ StDev & 65.089 & 4047 & 2025.959 & 10,265 & 0.004 & 103 & 0.159 & 0 \\ Min & 189.205 & 9300 & 1654.473 & 123,761 & 0.748 & 1240 & 1.000 & 0 \\ Max & 366.800 & 20,100 & 7036.044 & 147,152 & 0.761 & 1474 & 1.367 & 0 \\ Median & 255.373 & 11,100 & 6266.195 & 124,789 & 0.754 & 1251 & 1.064 & 0 \\ \hline \hline \multicolumn{8}{c}{**ACO\({}_{\mathbb{R}}\)**} \\ \hline Metric & CT [s] & CFEs & COST & LEN [m] & SMOO & NPTS & EEE [kWh] & NCOL \\ Mean & 98.591 & 21,350 & 645.898 & 93,431 & 0.805 & 938 & 0.600 & 0 \\ StDev & 8.851 & 3823 & 2.413 & 14 & 0.022 & 0 & 0.000 & 0 \\ Min & 91.010 & 16,850 & 641.955 & 93,422 & 0.770 & 937 & 0.600 & 0 \\ Max & 109.428 & 25,850 & 648.173 & 93,458 & 0.831 & 938 & 0.600 & 0 \\ Median & 91.678 & 20,150 & 646.829 & 93,424 & 0.817 & 938 & 0.600 & 0 \\ \hline \hline \multicolumn{8}{c}{**PSO**} \\ \hline Metric & CT [s] & CFEs & COST & LEN [m] & SMOO & NPTS & EEE [kWh] & NCOL \\ Mean & 130.876 & 18,560 & 2646.573 & 189,100 & 0.742 & 1893 & 2.885 & 0 \\ StDev & 6.761 & 1651 & 1161.778 & 50,969 & 0.031 & 509 & 1.347 & 0 \\ Min & 124.214 & 16,800 & 669.976 & 97,770 & 0.717 & 981 & 0.606 & 0 \\ Max & 141.255 & 21,200 & 3822.815 & 237,134 & 0.801 & 2373 & 4.258 & 0 \\ Median & 128.627 & 18,800 & 2503.187 & 190,055 & 0.728 & 1904 & 2.655 & 0 \\ \hline \multicolumn{8}{c}{**GA**} \\ \hline Metric & CT [s] & CFEs & COST & LEN [m] & SMOO & NPTS & EEE [kWh] & NCOL \\ Mean & 227.780 & 32,070 & 2228.843 & 175,115 & 0.725 & 1754 & 2.491 & 0 \\ StDev & 35.827 & 1140 & 360.917 & 18,078 & 0.005 & 181 & 0.494 & 0 \\ Min & 171.256 & 31,500 & 1638.002 & 147.969 & 0.721 & 1483 & 1.663 & 0 \\ Max & 258.184 & 34,350 & 2607.089 & 195,170 & 0.733 & 1955 & 2.997 & 0 \\ Median & 251.762 & 31,500 & 2180.302 & 170,733 & 0.726 & 1710 & 2.453 & 0 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Basic statistical measures for the 1 st test case and all 5 runs. Once the importance hierarchy of the metrics was determined, each algorithm was evaluated by assigning a score to its metrics based on a point system, structured as follows:  
* 3 points are granted for the best mean value of a selected metric, so, depending on the metric, the lowest (LEN, for example) or the highest (SMOO, for example) mean value for each algorithm. * the 2 nd receives 2 points. * the 3 rd receives 1 point. * the last (worst) result receives 0 points. Next, the final quantitative evaluation was achieved by calculating the weighted sum of the metric scores, combining their assigned weights with their respective grades, i.e. :  
\[G^{f}=\sum_{n=1}^{N}\omega_{n}G_{n} \tag{31}\]  
where \(G^{f}\) represents the overall score in points, \(\omega_{n}\) corresponds to the weight assigned to the \(n\)-th metric, and \(G_{n}\) denotes the score of the \(n\)-th metric. The total number of criteria (metrics) is indicated by \(N\). The evaluation results are detailed in Table 6. Based on the specified criteria, ACO\({}_{\mathbb{R}}\) emerges as the most effective algorithm for GPP, with PSO ranking as the second one. The GA achieves the highest scores concerning repeatability, making it the most suitable choice for fully autonomous planning scenarios.