# Mission Planning of UAVs and UGV for Building Inspection in Rural Area
## 5 Conclusions  
This paper addresses MPP for building inspection by establishing a task planning model for coordinated inspection using UGV and UAVs. Firstly, the MPP problem is described, which involves dispatching UGV to designated location and utilizing multiple UAVs to inspect all buildings within a certain area. Secondly, a mathematical optimization model for the MPP is established, aiming to minimize the mission time. The constraints include ensuring coverage of all buildings and maintaining sufficient battery power for the UAVs. Additionally, the model takes into full account the impact of varying operational time requirements for different mission points. Subsequently, the location of parking spots is determined based on the positions of the mission points and the operational time required. Finally, to address the issues of weak search capabilities in ACO and the tendency of GA to get trapped in local optima, the paper proposes an ACO-GA hybrid approach.  
The simulation results and comparisons with other algorithms demonstrate the superiority and effectiveness of the proposed ACO-GA, effectively addressing the task planning problem for building inspections. While this study focuses only on the task planning problem with a single parking point, the process of using UAVs for building inspections at individual task points has not been thoroughly studied, and real-world applications often involve inspection over large areas, requiring task planning with multiple parking points. In addition, the research findings of this paper primarily remain in the stages of algorithm design and simulation verification. Its applicability in the real world remains to be validated. Therefore, future research will focus on how UAVs can conduct inspections within individual buildings. And it is necessary to continue to explore solutions to task planning issues in larger areas and utilize actual data for verification and experimental validation of the effectiveness of the methods presented in this paper.  
Conceptualization, X.C. and Y.W.; methodology, X.C.; software, X.C.; validation, X.C., Y.W. and S.X.; formal analysis, Y.W.; investigation, S.X.; resources, X.C.; data curation, X.C.; writing--original draft preparation, X.C.; writing--review and editing, Y.W.; visualization, S.X.; supervision, Y.W.; project administration, Y.W.; funding acquisition, S.X. All authors have read and agreed to the published version of the manuscript.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Number of UAVs** & **3** & **4** & **5** & **6** & **7** \\ \hline Average time consuming (s) & 1686.17 & 1060.20 & 787.69 & 698.63 & 614.55 \\ Average cost (\(\hat{\tau}\)) & 1165.85 & 1304.08 & 1143.85 & 1319.18 & 1480.19 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Time consumption and costs under different numbers of UAVs.  
**Funding:** This research was funded by the National Natural Science Foundation of China, grant number 52102453, and the APC was fund by State Key Laboratory of Efficient Production of Forest Resources, Key Laboratory of National Forestry and Grassland Administration on Forestry Equipment and Automation, and Fundamental Research Funds for the Central Universities, grant number BLX202224.  
**Data Availability Statement:** Data are contained within the article.  
**Conflicts of Interest:** The authors declare no conflicts of interest.