# Path Planning of Intelligent Mobile Robots with an Improved RRT Algorithm
## 4 Testing in Real-World Scenarios
### Experiment and Result Analysis  
The actual experimental scenario chosen for this algorithm is shown in Figure 15. In this experimental scenario, two types of different obstacles are selected to verify the robot's ability to adjust its steering angles and generate smooth paths under different conditions. The robot's path planning time and path planning distance are used as evaluation metrics. The actual measurement shows that the shortest path distance is 3.3 \(\pm\) 0.2 m. In the experimental scenario of Figure 15, 20 sets of experiments are repeated, and the experimental results are recorded. The data is visualized using MATLAB 2024a, and the results are compared with the actual shortest path to validate the accuracy and feasibility of the algorithm.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Name** & **Parameter** \\ \hline Operating system & Linux Ubuntu 22.04 LTS (Jammy) \\ ROS & ROS2 Humble Hawksbill \\ Programming language & C++ \\ Control language & Linux language \\ \hline \hline \end{tabular}
\end{table}
Table 6: Experimental platform configuration and parameters.  
Figure 14: Mobile intelligent robot.  
Figure 15: Experimental scene diagram. The numbers from 1 to 5 represent different types of obstacles.  
The trajectory of the mobile intelligent robot in the experiment is shown in Figure 16.  
From Figure 16, it can be seen that Figure 16a,f are the starting and ending points of the robot path planning, while Figure 16b-e show the path planning process of the mobile intelligent robot. In Figure 16b,d, when the robot encounters an obstacle, it turns to the original direction through a collision detection algorithm and turns to the endpoint direction under the constraint of the turning angle range, greatly avoiding turning to the other three directions. At the same time, the robot uses a Kalman filter to filter redundant target detection points, making its planning speed faster. The obtained target path points are smoothed through Bezier curves to obtain the final path, which reduces the number of turns and avoids sharp turns, improving the smoothness and stability of the robot's motion. Figures 17 and 18 are box plots of robot path planning time and path planning length obtained from 20 sets of experiments, respectively.  
Figure 16: Path planning of improved algorithm for mobile robot. (**a**) The robot is located at the starting point. (**b**) The robot travels to obstacle 1. (**c**) The robot avoids obstacle 1. (**d**) The robot travels to obstacle 2. (**e**) The robot moves towards the target point. (**f**) The robot reaches the target point.  
Figure 17: Boxplot of robot path planning time.  
Figure 18: Boxplot of robot path planning length.  
From Figures 17 and 18, it can be perceived that the average planning time of the mobile robot remained stable at 17.017 s, while the average path planning length was 3.413 m across 20 experiments. Compared to the known measured path length, the planned path remained stable between 3.310-3.485 m and the planning time remained stable between 16.550-17.425 s. This demonstrates that the improved algorithm offers both high stability and accuracy. During the path planning process, the robot may be influenced by physical factors such as light and temperature. In the future, better results could be achieved through optimization of the hardware components.