# UAV Path Planning Model Based on R5 DOS Model Improved A-Star Algorithm
## 3 Results and Discussion  
Based on the above path planning algorithm, we created different simulation environments in Matlab. We set the starting point as the coordinate origin and the location of the target point varies with the size of the map, usually the point farthest from the starting point in the map. In order to simulate the real situation, the number, position and size of obstacles are generated randomly.  
Due to the complexity of the improved A-star path planning algorithms, we need to use the computing time to evaluate the efficiency of the improved A-star algorithm in the process of path planning and obstacle avoidance for maps of different specifications.  
The CPU of our computer is Intel(R) Core(TM) i7-8750H CPU @ 2.20 GHz 2.21 GHz and the GPU is NVIDIA GTX 1060 Max-Q 6 GB. We have carried out eighty simulation experiments in MATLAB 2022A and taken the average value. To determine the potential advantages of the improved path planning algorithm in a random map, we fixed the altitude at 15 m.  
To determine the potential advantages of the improved path planning algorithm in random maps, we fixed the height to 15 m and simulated the improved A-star algorithm in maps with two options for length and width, 30 m \(\times\) 90 m and 50 m \(\times\) 150 m, respectively. The results are shown in Figures 8 and 9.  
After eighty simulations, we can see that, compared with the A-star algorithm, the improved A-star algorithm has fewer turning angles, a smoother flight path and a good effect on obstacle avoidance. To further calculate the advantages of the improved A-star  
Figure 8: The fitting result of the improved algorithm on a 30 m \(\times\) 90 m size map.  
Figure 9: The fitting result of the improved algorithm on a 50 \(\times\) 150 size map.  
Figure 11: Comparison of moving paths between A-star algorithm and A-star Plus algorithm.  
Figure 12: Comparison of explore node between A-star algorithm and A-star Plus algorithm.  
It is clear that the A-star algorithm grows exponentially as the map grows larger, while the improved A-star algorithm explores very small nodes and computation times. In 2021, [PERSON] et al. [23] proposed an improved geometric A-star algorithm. Compared with the traditional A-star algorithm, the number of nodes is reduced by more than 19%, the total distance is reduced by more than 13% and the running time is reduced by 41.1%. As can be seen from Table 3, using a map size of 50 m \(\times\) 100 m as an example, the improved A-star algorithm reduces the number of nodes by approximately 99.18%, the total distance by about 5.30% and the calculation time by about 99.72%. In addition, the number of nodes is reduced by approximately 99.18%, the total distance decreases by about 5.30% and the calculation time decreases by about 99.72%.  
The larger the specifications of the map, the better the effect of the obstacle-improved A-star algorithm.  
Overall, the improved A-star algorithm clearly performs better in all examples, with better parameters, calculation times and probe nodes.  
Figure 13: Comparison of calculation time between A-star algorithm and A-star Plus algorithm.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Path Parameters** & \begin{tabular}{c} **Traditional** \\ **A-Star** \\ \end{tabular} & **A-Star Plus** & \
\begin{tabular}{c} **Geometric** \\ **A-Star Reduced** \\ **Proportion** \\ \end{tabular} &
\begin{tabular}{c} **A-Star Plus** \\ **Reduced** \\ **Proportion** \\ \end{tabular} \\ \hline Running time/s & 1184.188 & 0.26625 & 41.1\% & 99.98\% \\ Number of nodes & 157,550 & 514 & 24.2\% & 99.7\% \\ Total distance/s & 112.177 & 106.075 & 15\% & 5.4\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of A-star Plus algorithm with geometric A-star algorithm and traditional A-star data.