# An Improved Pied Kingfisher Optimizer for Maritime UAV Path Planning
## 4 UAVS Path Planning Through IPKO
### Path Planning Experiment  
Experiments were conducted using the map parameters provided in Table 6 throughout the UAV path planning optimization process, where various algorithms demonstrated varying performances and outcomes. The experimental findings indicate that the IPKO algorithm offers significant advantages compared to other algorithms. Firstly, as depicted in Figure 9, after 50 iterations, the flight cost function value of IPKO is the most minimal among all algorithms, showcasing its superior optimization performance in practical applications. Figure 10 demonstrates that the IPKO algorithm consistently performs effectively during actual laboratory flights, with subfigure (a) showing the main view, subfigure (b) showing the front view, and subfigure (c) showing the top view.  
As shown in Table 7, the comparative analysis reveals that the path generated using the IPKO is not only shorter but also smoother. The cost value for IPKO is 117.67, while it is 121.38 for PKO and 132.38 for DOA. Compared to PKO, there is an approximate reduction in cost of 5.39%; moreover, compared to SCA, the reduction is approximately 19.86%. These findings indicate that the IPKO algorithm strikes a better balance between flight distance and path smoothness during optimization, minimizing unnecessary sharp turns and oscillations, thereby enhancing the stability and safety of UAV flight. Furthermore, the comparative analysis of paths further validates the potential applicability of the IPKO algorithm as it consistently generates high-quality paths and enhances operational performance in complex environments for UAVs. Moreover, laboratory flight tests are conducted using quadrotor aircraft to evaluate the effectiveness of the IPKO algorithm in real-flight scenarios, which demonstrates its potential application in maritime rescue missions.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Threat Zone** & **Threat** & **Starting** & **Destination** \\
**Center Coordinates** & **Zone Radius** & **Coordinates** & **Coordinates** \\ \hline (150, 150, 0) & 30 & (0, 0, 20) & (200, 200, 20) \\ (50, 50, 0) & 20 & \(\color[rgb]{0,0,0}\definecolor[named]{pgfstrokecolor}{rgb}{0,0,0} \pgfsys@color@gray@stroke{0}\pgfsys@color@gray@fill{0}\text{{---}}\) & \(\color[rgb]{0,0,0}\definecolor[named]{pgfstrokecolor}{rgb}{0,0,0} \pgfsys@color@gray@stroke{0}\pgfsys@color@gray@fill{0}\text{{---}}\) \\ (100, 100, 0) & 20 & \(\color[rgb]{0,0,0}\definecolor[named]{pgfstrokecolor}{rgb}{0,0,0} \pgfsys@color@gray@stroke{0}\pgfsys@color@gray@fill{0}\text{{---}}\) \\ (100, 150, 0) & 20 & \(\color[rgb]{0,0,0}\definecolor[named]{pgfstrokecolor}{rgb}{0,0,0} \pgfsys@color@gray@stroke{0}\pgfsys@color@gray@fill{0}\text{{---}}\) \\ (50, 150, 0) & 20 & \(\color[rgb]{0,0,0}\definecolor[named]{pgfstrokecolor}{rgb}{0,0,0} \pgfsys@color@gray@stroke{0}\pgfsys@color@gray@fill{0}\text{{---}}\) \\ (150, 50, 0) & 20 & \(\color[rgb]{0,0,0}\definecolor[named]{pgfstrokecolor}{rgb}{0,0,0} \pgfsys@color@gray@stroke{0}\pgfsys@color@gray@fill{0}\text{{---}}\) \\ \hline \hline \end{tabular}
\end{table}
Table 6: UAV experimental map parameter table.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Algorithm** & **IPKO** & **PKO** & **DOA** & **PSO** & **WOA** & **SCA** & **SO** & **GWO** \\ \hline Minimum Cost In Simulation & 119.97 & 127.32 & 195.05 & 137.13 & 131.67 & 245.84 & 133.31 & 134.06 \\ \hline Minimum Cos In Reality & 208.50 & 210.74 & 220.56 & 227.82 & 216.87 & 237.22 & 214.93 & 216.41 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Minimum cost values for each algorithm.  
Figure 9: Drone trajectories with different algorithms.