# RJA-Star Algorithm for UAV Path Planning Based on Improved RSDOS Model
## 2 Materials and Methods
### Improved Path Planning Algorithm
The experimental results show that the RJA star algorithm reduces the mobile path by 2.53%, the computation time by 97.65%, the number of nodes by 99.96% and the number of corners by 96.08% with the maximum corners reduced by approximately 63.30%.smaller angles and smoother paths. In general, our method can effectively reduce the number of turns, calculate the nodes and the angle of turns in the process of UAV motion. The contributions of this study are as follows:

1. In order to solve the problem of high computational complexity of three-dimensional A-star algorithm, RJA star path algorithm is proposed. 2. The pretreatment process is added to screen out a batch of hops and optimize the path between hops. The access and calculation time of nodes are reduced, and the calculation speed is improved. 3. When obstacles are detected, the algorithm generates hops to optimize the moving path to avoid obstacles. The organizational structure of this paper is: In the Section 2, we introduce the specific improved form of RJA star algorithm and pseudo code. In Section 3, we simulated in a 3D map to verify the progressiveness of our algorithm. Finally, we draw conclusions in Section 4 and conceive the future work. The RJA-star algorithm mainly works offline on already known scenarios, without considering the fact that the actual working environment of the UAV should be dynamic and complex. Future work should focus on studying complex dynamic scenarios, including the consideration of flying birds and moving agricultural machinery and other factors. Conceptualization, J.L. and W.Z. ; methodology, J.L. and Y.H. ; software, S.F. and C.L. ; validation, S.F., C.L. and W.Y. ; data curation, C.L. ; writing--original draft preparation, W.Z. ; writing--review and editing, W.Z. ; funding acquisition, J.L. and Y.H. All authors have read and agreed to the published version of the manuscript. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Path Parameters** & **Traditional** & & **Geometric** & **RJA-Star** \\
**A-Star** & & & **A-Star Reduced** & **Reduced** \\  & & & **Proportion** & **Proportion** \\ \hline Average running time/s & 475.785 & 11.184 & 41.1\% & 97.65\% \\ Average number of nodes & 100,609 & 81 & 19\% & 99.96\% \\ Total distance/s & 150.335 & 146.839 & 13\% & 2.53\% \\ Max turning & 90\({}^{\circ}\) & 33.024\({}^{\circ}\) & 66.7\% & 63.30\% \\ Number of turns & 56 & 2 & 84.1\% & 96.08\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of indexes of three algorithms. **Funding:** This research was funded by Jilin Province Development and Reform Commission China, grant number 2020C037-7; The Education Department of Jilin Province China, grant number JJKH20220332 KJ; The Science and Technology Project of Education Department of Jilin Province grant number JJKH20220330 KJ; Changchun Science and Technology Development Plan China, grant number 21 ZGN26. **Data Availability Statement:** Not applicable. **Conflicts of Interest:** The authors declare no conflict of interest. ## References

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