## 5 Simulation Experiment and Analysis
### The First Simulation Environment  
In the first simulation environment, a 30 \(\times\) 30 raster map is used, featuring obstacles, navigable areas, and start/target points as per Section 3.2. Figure 9 shows path planning  
Figure 8: Flowchart of IEHA algorithm.  
diagrams for each algorithm in this environment. Table 1 offers a comparative analysis of the simulation results. In terms of average path length, the IEHA algorithm proposed in this paper is slightly better than ACO. ACO reduces the path length by 1.2% compared to IEHA; however, it incurs 11 more collisions, and the total number of collisions increases by 37.5% compared to the traditional A * algorithm. The core variables comprehensively considered for UAV monitoring of carbon dioxide emissions from heavy vehicles in the harbor area are the cost of UAV procurement and the length of the monitoring path, influencing the total economic and environmental costs, respectively. Thus, in the context of this paper's research, the improved algorithm (IEHA) exhibits superior performance in the first simulation environment.  
To further verify the robustness of the proposed algorithm (IEHA), the starting and target points of the previous environmental model were adjusted. Figure 10 illustrates the path planning diagrams for each algorithm within the second simulation environment. Table 2 presents a comparative analysis of the results from the second simulation environment. Compared to the traditional A* algorithm, the improved algorithm (IEHA) in this paper significantly reduces the number of turns and shortens the path length by 61.5 % and 4.5 %, respectively. The collision count is minimized to zero occurrences, greatly enhancing the safety and reliability of the path. In the average path length metric, the improved algorithm (IEHA) outperforms the other three algorithms, with both its average and minimum path lengths being shorter. It also reduces the average turning count by 61.5% compared to the traditional A* algorithm and by 58.3% compared to the ACO algorithm. Additionally, the improved algorithm (IEHA) achieves an average collision count of zero.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{4}{c}{Simulation Experiment One} \\ \hline Algorithm & Simulation Count & Average Turning Count & Average Collision Count \\ \hline A* & 10 & 12 & 8 \\ \hline IEA & 10 & 16 & 0 \\ \hline ACO & 10 & 12 & 11 \\ \hline IEHA & 10 & 10 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Performance comparison of algorithms.  
Figure 9: Comparison of planning paths by algorithms.  
\begin{table}
\begin{tabular}{c c c c} \hline \multicolumn{4}{c}{Simulation Experiment Two} \\ \hline Algorithm & Simulation Count & \begin{tabular}{c} Average Turning \\ Count \\ \end{tabular} & \begin{tabular}{c} Average Collision \\ Count \\ \end{tabular} \\ \hline A* & 10 & 13 & 8 \\ \hline IEA & 10 & 18 & 1 \\ \hline ACO & 10 & 12 & 6 \\ \hline IEHA & 10 & 5 & 0 \\ \hline \multicolumn{4}{c}{Simulation Experiment Two} \\ \hline Algorithm & \begin{tabular}{c} Average Path \\ Length \\ \end{tabular} & \
\begin{tabular}{c} Minimum Path \\ Length \\ \end{tabular} &
\begin{tabular}{c} Maximum Path \\ Length \\ \end{tabular} \\ \hline A* & 44.53 & 44.53 & 44.53 \\ \hline IEA & 46.28 & 46.28 & 46.28 \\ \hline ACO & 46.53 & 44.53 & 48.18 \\ \hline IEHA & 42.52 & 42.52 & 42.52 \\ \hline \end{tabular}
\end{table}
Table 1: _Cont._  
Figure 10: Comparison of planning paths by algorithms.