# Application of Angle Related Cost Function Optimization for Dynamic Path Planning Algorithm
## 5 Experiments
### Results and Discussion  
In this paper, multi-time experiments were carried out, and the number of nodes, running times, and path information of different algorithms were recorded and analyzed in detail.  
In order to select the representative experimental data for analysis, this paper selected the results of three experiments with the theoretical shortest path lengths, which had different numbers of search nodes and increments. The detailed data recorded in these three experiments were as follows.  
In the first experiment, the node ID 34,091 was the starting point, the node ID 34,201 was the end point, the four algorithms sought solutions, and the shortest path length was 2290.95 m, The data records of the algorithm solution are shown in Table 2.  
In the second experiment, node ID was the 33,898 is starting point, node ID 34,100 was the end point, the four algorithms sought solutions, and the shortest path length is 2832.47 m, The data records of the algorithm solution are shown in Table 3.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Evaluation Standard** & **Dijkstra** & **A* & **ACA** & **SODPP** \\ \hline Start Point & 34,091 & 34,091 & 34,091 & 34,091 \\ End Point & 34,201 & 34,201 & 34,201 & 34,201 \\ Total Points & 826 & 826 & 826 & 826 \\ Load Points & 826 & 826 & 826 & 42 \\ Load Time & 851 ms & 836 ms & 801 ms & 151 ms \\ Construct Time & 1162 ms & 1167 ms & 1087 ms & 126 ms \\ Search Points & 76 & 37 & 82 & 23 \\ Total Time & 17 ms & 9 ms & 63 ms & 5 ms \\ Path Length & 2290.95 m & 2290.95 m & 2295.08 m & 2290.95 m \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of results for Experiment 1.  
Figure 10: Karamay city.  
In the third experiment, the node ID was 33,766 for the starting point, the node ID was 33,966 for the end point, the four algorithms sought solutions, and the shortest path length was 21415.83 m, The data records of the algorithm solution are shown in Table 4.  
Compared with the experimental data, the ant colony algorithm had the lowest time efficiency, but our algorithm was the best. In the road network load scale, besides the SODPP algorithm, the other three algorithms need to load all of the nodes. Loading nodes is time consuming and the time taken to build a road network is relatively large. In terms of the number of algorithm search nodes, the number of nodes searched by the Dijkstra algorithm is larger, and the number of nodes searched by the A* algorithm and SODPP algorithm are less. In regard to the path quality, apart from ant colony algorithm, the other algorithms are able to find the shortest path.  
Based on the analysis of the principle of the algorithm and the experimental data, we found that when the SODPP algorithm was used to load the nodes and build the network, the time was greatly reduced, indicating that the improvement of the sub-net is effective. In the analysis of the experimental data of the SODPP algorithm with three experimental datasets, we found that the size of the road network, the number of search nodes, and the time-consumption required to seek the shortest path were significantly reduced. In contrast, compared with the Dijkstra algorithm, A* algorithm and ant colony algorithm, the SODPP algorithm was shown to effectively improve the time required for map loading and choosing the optimal path to solve the real-time requirements of real-time navigation systems.