## 7 Discussion
### Limitations and Future Work  
The voltage of the battery supplying power to the FPTLIR gradually decreases during mission execution, resulting in consequential variations in its performance and subsequent impact on energy consumption. This paper primarily focuses on modeling the energy demand side of the FPTLIR during mission paths; however, it does not fully consider the influence of battery voltage variations. Therefore, future studies should include variations in battery-related parameters to better understand their impact on the FPTLIR energy consumption.  
In addition, considering the complexity of multiobjective optimization models based on flight energy models, we chose to study only the simplest sequence of flight state transition times when dividing path intervals by flight states. However, the precision with which state intervals are partitioned has a significant effect on optimization effectiveness and speed. As the partitioning accuracy increases, optimization effectiveness improves but at the cost of increased optimization time. In future research, these two effects should be comprehensively considered to moderately improve the precision of state interval partitioning to better optimize the FPTLIR energy consumption.  
The computational efficiency of algorithms is also crucial for real-time application scenarios. In particular, for real-world applications that require rapid response and decision making, the complexity of the algorithm and the time required for optimization become factors that cannot be ignored. With this in mind, future research will focus on deploying the  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Algorithm/Parameter** & **Iterations** & **1** & **10** & **20** & **30** & **40** & **50** \\ \hline \multirow{3}{*}{Improved NSGA-II} & GD & 0.131 & 0.042 & 0.018 & 0.006 & 0.007 & 0.004 \\  & Mean HV & 0.542 & 0.727 & 0.784 & 0.899 & 0.972 & 0.989 \\  & Pareto front size & 35 & 44 & 49 & 52 & 50 & 50 \\  & Running time (s) & 0.846 & 4.225 & 7.624 & 12.342 & 17.563 & 22.364 \\ \hline \multirow{3}{*}{Standard NSGA-II} & GD & 0.216 & 0.124 & 0.101 & 0.086 & 0.057 & 0.036 \\  & Mean HV & 0.421 & 0.542 & 0.692 & 0.684 & 0.724 & 0.821 \\  & Pareto front size & 21 & 33 & 42 & 39 & 41 & 45 \\  & Running time (s) & 0.692 & 3.964 & 6.017 & 8.117 & 12.031 & 16.247 \\ \hline \multirow{3}{*}{MOPSO} & GD & 0.203 & 0.152 & 0.128 & 0.093 & 0.059 & 0.057 \\  & Mean HV & 0.433 & 0.494 & 0.511 & 0.753 & 0.724 & 0.805 \\ \cline{1-1}  & Pareto front size & 22 & 29 & 36 & 43 & 41 & 43 \\ \cline{1-1}  & Running time (s) & 0.765 & 3.721 & 5.924 & 8.214 & 11.220 & 15.434 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The performance metrics at different iterations.  
methods in this paper in real-time application scenarios and improving the computational efficiency of the NSGA-II algorithm to meet the demands of practical applications. In addition, given the specificity and complexity of transmission line inspection, exploration of algorithm customization for specific application scenarios will be pursued to ensure more efficient and accurate optimization results, thereby providing a more effective and reliable technical solution in the field of transmission line inspection.