# Dynamic Path Planning for Unmanned Surface Vehicles with a Modified Neuronal Genetic Algorithm
## 5 Results and Analysis
### Overall Analysis  
Table 1 shows a summary of the experimental results from a static environment for the baseline and proposed methods. Table 2 shows the result for the dynamic environment. The total genome, distance traveled, and time traveled are average values for all experiments with varying mutation rate values. Distance is expressed in distance units and time is expressed in time units in the simulator. Detailed experimental results for each mutation rate, total generation, and current genome value are provided in Appendix A.  
From Table 1, we can see that, in general, the proposed method can produce convergent solutions more quickly (requiring fewer total genomes) than the baseline method can. These positive results were achieved for experiments with three, five, and seven inputs. The USV with seven sensor inputs shows the most significant difference (1.64 times faster). For  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Input**} & \multicolumn{3}{c}{**Baseline (NNGA)**} & \multicolumn{3}{c}{**Proposed (Modified Neuronal GA)**} \\ \cline{2-7}  & \multicolumn{1}{c}{**Total**} & \multirow{2}{*}{**Distance**} & \multirow{2}{*}{**Time**} & \multicolumn{1}{c}{**Total**} & \multirow{2}{*}{**Distance**} & \multirow{2}{*}{**Time**} \\  & **Genome** & & & & & \\ \hline
3 Input & 354.83 & 259.68 & 64.29 & 239.33 & 262.77 & 54.11 \\
5 Input & 301.67 & 255.98 & 46.68 & 291.83 & 257.53 & 50.86 \\
7 Input & 307.50 & 271.50 & 55.79 & 187.50 & 266.74 & 69.27 \\ Mean & 321.33 & 262.39 & 55.58 & 239.56 & 262.35 & 58.08 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of experimental results from a static environment for the baseline and proposed methods.  
Figure 12: Examples of experimental results in dynamic environments for the baseline method **(left)** and the proposed method (**right**) with five sensor inputs and a mutation rate value of 0.255 (this figure is based on Tables 10 and 11 in Appendix A).  
the travel distance parameters, overall, the proposed method is slightly better (produces shorter paths) compared to those of the baseline method. Because seven sensor inputs have a significant increase compared to three and five inputs, the average travel distance is better using the proposed method. The detailed results in the table in Appendix A also show that the proposed method is superior for travel distance in many experiments. Nevertheless, for travel time parameters, the proposed method does not show better results. Only at input sensor three does the proposed method show a faster travel time. However, the decline in performance was declared not significant, as proven by the significance test in Section 5.3. Therefore, we can indicate that in this static case, the proposed method can still compete because it is superior in terms of travel distance parameters. Moreover, this study has limitations for not considering other USV performance parameters such as stability and smoothness. USVs with advantages in the distance aspect are suitable for implementing victim evacuation schemes, such as those in flood disasters, accidents at sea, and many more.  
When compared with experiments conducted in a static environment, those conducted with the proposed method show better performance in a dynamic environment. For all input number sensor values, the proposed method can produce a convergent solution around 1.38 times faster (and requires less total genome) compared to that with the baseline method. Moreover, for all input number values, the proposed method is also around 1.26 times faster in terms of travel time compared to that under the baseline method. However, for all input numbers, the proposed method produces a longer travel distance. This longer distance is covered more quickly, indicating that in the proposed method, the USV moves with a higher acceleration value compared to that under the baseline method. Therefore, the proposed method produces faster solutions than does the baseline method.