## 3 Methods
### Improved DWA Algorithm
#### 3.2.3 Simulation Experiment  
In order to compare the environmental adaptability of the DWA algorithm and the improved DWA algorithm, 50 path planning simulation experiments are conducted in three experimental scenarios. The relevant parameter configuration is shown in Table 3, and the simulation experiment data are shown in Table 4. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Rule Number**} & \multicolumn{2}{c}{**Input**} & \multicolumn{3}{c}{**Output**} \\ \cline{2-6}  & **G** & **O** & \(\alpha\) & \(\beta\) & \(\gamma\) \\ \hline
1 & S & S & M & M & S \\
2 & S & M & L & M & S \\
3 & S & L & L & S & S \\
4 & M & S & M & M & S \\
5 & M & M & M & M & M \\
6 & M & L & M & S & M \\
7 & L & S & S & L & M \\
8 & L & M & S & L & M \\
9 & L & L & L & M & L \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Table of fuzzy rules. Figure 6: Membership functions for fuzzy sets G and O. (**a**) G’s membership function; (**b**) O’s membership function. Figure 7: The membership function of \(\alpha\), \(\beta\), and \(\gamma\). (**a**) \(\alpha\)’s membership function. (**b**) \(\beta\)’s membership function. (**c**) \(\gamma\)’s membership function. In the Scenario 1 experiment, as shown in Figure 8, where the red rectangle represents the start point, and the yellow circle represents the target point. From the curve of change, the vehicle moves towards the obstacle and is far from the target point, the fuzzy controller prioritizes obstacle avoidance and outputs a larger \(\beta\), a smaller \(\alpha\), and a moderate \(\gamma\). As the vehicle gradually approaches the target point, around the number of iterations 360, the vehicle begins to approach the target point and moves away from obstacles. Finally, the fuzzy controller outputs a larger \(\alpha\), a moderate \(\beta\), and a smaller \(\gamma\) to ensure that the vehicle slowly approaches the target point. In the Scenario 2 comparison experiment, as shown in Figure 9, where the red rectangle represents the start point, and the yellow circle represents the target point. The distance between the initial vehicle and the obstacle and the target point is large, and the output of the fuzzy controller is a larger \(\alpha\) and \(\gamma\), and a moderate \(\beta\), accelerating towards the target point. As the vehicle approaches the obstacle, priority is given to obstacle avoidance (the number of iterations is about 150), the \(\beta\) gradually increases, and the \(\alpha\) and \(\gamma\) gradually decrease. As the vehicle gradually approaches the target point and moves away from the obstacle (about 375 iterations), the distance between the vehicle and the obstacle and the distance to the target point are both moderate and, at this time, the output is a moderate \(\alpha\) and \(\beta\), and a smaller \(\gamma\). \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Experiment** & **Algorithm** & **Planning Time (s)** & **Path Length** & **Success Rate** \\ \hline \multirow{2}{*}{Scenario 1} & DWA & 235.172 & 196.342 & 90\% \\  & improved DWA & 246.374 & 184.751 & 96\% \\ \hline \multirow{2}{*}{Scenario 2} & DWA & 219.532 & 174.285 & 94\% \\  & improved DWA & 224.784 & 170.764 & 98\% \\ \hline \multirow{2}{*}{Scenario 3} & DWA & 220.429 & 175.479 & 86\% \\  & improved DWA & 226.425 & 169.427 & 96\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Simulation experiment data results of three experimental environments. Figure 8: Simulation experiment results of Scenario 1. (**a**) The resulting graph of the path comparison experiment; (**b**) output variation chart. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Value** & **Parameter** & **Value** \\ \hline \(\alpha\) & 0.15 & Maximum angular rate & 60 rad/s \\ \(\beta\) & 0.4 & Maximum linear acceleration & 0.1 m/s\({}^{2}\) \\ \(\gamma\) & 0.3 & Trajectory prediction time & 2 s \\ Maximum linear speed & 1 m/s & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Parameter configuration of simulation experiment.