## 5 Experimental Results and Analysis
### Comparison of Experimental Results and Analysis  
There is some discrepancy between the theoretical and real maps because the ground in the experimental setting is quite smooth, meaning there is less friction, and the mobile robot will slide a little as it advances. Three distinct goal sites and the same beginning point are selected for the experiment. The starting point of path planning is the upper left corner of the map, the target point G1 is the lower left corner, G2 is the lower right corner, and G3 is the upper right corner. The final path planned by the A* algorithm and the mixed A* algorithm is shown in Figure 33.  
The A* algorithms and the Hybrid A* algorithm's route planning times under various target point circumstances are shown in Table 7. As can be seen, the Hybrid A* algorithm's route planning time is smoother and shorter than the original A* algorithm's, resulting in smoother robot movement and a lower need for precise robot control. Figure 33: The performance of the path planning task by the robot in a real environment via different algorithms: (**a**) A* algorithm to goal 1; (**b**) A* algorithm to goal 2; (**c**) A* algorithm to goal 3; (**d**) Hybrid A* algorithm to goal 1, (**e**) Hybrid A* algorithm to goal 2; (**f**) Hybrid A* algorithm to goal 3. Figure 32: The test scene’s map. (**a**) The actual setting. (**b**) The actual environment and the theoretical environment match. The conventional A* algorithm's route has several turning segments and significant turning degrees, which increases path redundancy, significantly lowers motor life and operating efficiency, and prevents the mobile robot from walking. The robot avoids obstacles in the general direction of both local and global route planning to complete navigation tasks as quickly as feasible. The smoothness of the route is guaranteed, the turning angle and redundancy spots are efficiently decreased, and the path's length and smoothness are maximized by using the fusion technique suggested in this research. It is necessary to include dynamic barriers in the route that the fusion algorithm plans since the mobile robot may run into them while walking. Figure 34 displays the route that the mobile robot has planned. The mobile robot's path-planning procedure at the start, midpoint, and end of obstacle avoidance is shown in the figure. The dynamic obstacle route and dynamic obstacle are represented in the figure by the yellow curve and yellow square. The mobile robot will go along the prior course until it comes across a dynamic obstacle, as shown in Figure 34a. To complete local route planning, the mobile robot employs radar positioning for emergency obstacle avoidance and steers clear of dynamic impediments when they are present on the road. The mobile robot encounters intricate obstacles, employs its own sensor to determine its status, and completes its autonomous navigation using the hybrid route planning algorithm and AMCL positioning algorithm, as seen in Figure 34b. The fusion algorithm adds dynamic barriers to the route it plans since it recognizes that the mobile robot may run into them while walking. Before running into a dynamic obstacle, the mobile robot continues its prior course. If the road contains dynamic barriers, the radar will be used by the mobile robot to detect and steer clear of emergency obstacles. Figure 34c presents the experimental findings. Table 8 displays the outcomes of the experiment. The results demonstrate that the suggested hybrid approach may effectively finish the route planning depending on how smooth the experimental location is. The mobile robot will be impacted by the early marching and navigation. However, this has no effect on the final route planning and obstacle avoidance procedures. As Table 8 shows, under the same environment, the average time consumption of the hybrid algorithm in this paper is reduced by 10.11%, and the number of path inflection points is reduced by 37.5%.