# Research on AGV Path Planning Integrating an Improved A* Algorithm and DWA Algorithm
## 5 Simulation and Experimental Analysis  
To verify the feasibility, applicability, and reliability of the fusion algorithm proposed in this paper, simulation experiments were conducted to compare the alternative A* algorithm, the improved DWA algorithm, and the proposed fusion algorithm. MATLAB 2019b was used for programming. The raster map size was 20 \(\times\) 20, with each small raster unit set to 1 m in length and black rasters indicating obstacles. A dynamic obstacle was randomly added to the simulation environment to verify the dynamic obstacle avoidance performance of the fusion algorithm. To ensure the fairness of the simulation experiments, the same map was used for all experiments, and the starting and target points for each algorithm's path planning were also the same. In the simulation experiment, the initial weights of each evaluation subfunction were set as follows: \(\alpha\) = 0.2, \(\beta\) = 0.4, \(\gamma\) = 0.2, and \(\delta\) = 0.35. The speed parameters are set as follows (Table 2).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter name** & **Numerical Value** \\ \hline Maximum line speed \(v_{max}\) & 2 m/s \\ Maximum angular velocity \(\omega_{max}\) & 0.7 rad/s \\ Maximum linear acceleration \(\dot{v}_{max}\) & 0.4 m/s\({}^{2}\) \\ Maximum angular acceleration \(\dot{\omega}_{max}\) & 1.7 rad/s\({}^{2}\) \\ Projection time \(T\) & 3 s \\ \hline \hline \end{tabular}
\end{table}
Table 2: Speed parameter settings.  
Figure 8: Flowchart of fusion algorithm.  
The simulation results of the three algorithms are shown in Figure 9. The green grid represents the starting point, the red grid represents the target point, the yellow square simulates the dynamic obstacles, and the gray square simulates the unknown static obstacles.  
As shown in Figure 9, while the alternative A* algorithm can find the globally optimal solution during path planning, the mobile robot may collide with obstacles when navigating around the unknown obstacles, as it lacks the ability to avoid them. The improved DWA algorithm is effective at avoiding both known and unknown static obstacles during path planning, preventing collisions. However, the path it plans in a complex global environment is not optimal, making the AGV's global path more costly. This paper addressed these issues by combining the two algorithms into a new fusion algorithm, enhancing the AGV's ability to avoid dynamic obstacles. As shown in Figure 9a, the fusion algorithm not only finds the global optimal solution in the global path planning process but also effectively avoids unknown static and dynamic obstacles, improving the path safety. Additionally, the robot's path was smoother during movement.  
To further verify the effectiveness of the fusion algorithm, the path length, path search time, and the distance from the path to the nearest obstacle were used as performance metrics to quantitatively analyze the three algorithms mentioned above. The results are shown in Table 3.  
In this paper, the fusion algorithm reduces the path length by 0.111 m, shortens the path search time by 0.51 s compared to the improved DWA algorithm, and increases the shortest distance from the path to the obstacle by 0.14 m. The data in Table 3 demonstrate that the fusion algorithm performs well in global path planning, and its comprehensive performance is better than those of the other two algorithms.