# Path Planning of Intelligent Mobile Robots with an Improved RRT Algorithm
## 3 Experimental Validation
### Multi-Map Environment Path Planning Simulation Experiment  
In this section, the proposed improved algorithm is compared with the traditional RRT algorithm, with different control groups set for different maps to verify its effectiveness. In the simulation experiments, the improvement of the proposed algorithm is validated by comparing data such as the percentage change in average path length and average planning time, the average number of collisions, and the efficiency of valid samples. The red curve represents the final path before and after the improvement. The simulation results for the obstacle-free map are shown in Figure 7.  
From Figure 7a,b and Table 2, it can be seen that in the accessible map environment, the traditional RRT algorithm, due to its arbitrariness, causes the nodes to disperse to its surroundings randomly, and collisions may occur even in the accessible environment, which is more random, and it is unable to search for the shortest path between the initial point and the target point more accurately. Compared with the traditional RRT algorithm, the average planning time of the search path is reduced by 34.71%, the average path planning length is reduced by 10.76%, and in the case of almost the same valid points, the average sampling point tree is reduced significantly by 43.71% compared with the RRT algorithm, which improves the search efficiency. The above two sets of experiments show that the improved algorithm is more conducive to more accurate path planning with higher efficiency in an obstacle-free environment. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Title** & **Name** & **Parameter Configuration** \\ \hline Software & MATLAB & MATLAB R2024a \\ \hline \multirow{3}{*}{Hardware} & CPU & 12 th Gen Intel(R) Core(TM) i7-12650H \\  & GPU & NVIDIA GeForce RTX 3070 \\ \cline{1-1}  & Mainboard & Intel B660 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Hardware and software parameters. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Algorithm** & \begin{tabular}{c} **Average** \\ **Path** \\ **Length** \\ \end{tabular} & \begin{tabular}{c} **Average** \\ **Planning** \\ **Time** \\ \end{tabular} & \begin{tabular}{c} **Average** \\ **Number of** \\ **Collision** \\ **Points** \\ \end{tabular} & \
\begin{tabular}{c} **Average** \\ **Number of** \\ **Sampling** \\ **Points** \\ \end{tabular} &
\begin{tabular}{c} **Average** \\ **Number of** \\ **Active** \\ **Points** \\ \end{tabular} \\ \hline RRT & 39.159 & 0.103 & 2 & 151 & 37 \\ Improved algorithm & 34.945 & 0.067 & 0 & 85 & 33 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of accessibility map simulation data. Figure 7: Flow chart of the improved RTT-KF algorithm. In this figure, the blue triangle represents the starting point, the pink triangle represents the target point, the black dashed circle represents the target point area, The green dots represent the search nodes and the red solid line represents the final path from the starting point to the target point. (**a**) RRT algorithm; (**b**) improvement of the RRT-KF algorithm. The simulation results of the multi-channel map are shown in Figure 8.  
The channels are first labeled as shown in Figure 9. In the 40 sets of experiments in the multi-channel map, the traditional RRT algorithm accounts for 25.00%, 52.50%, and 17.50% of channel 1, channel 2, and channel 3, respectively, in the process of path planning, while the improved algorithm accounts for 100% of channel 2, where it can be seen that the decision-making planning of the paths becomes more accurate after incorporating Kalman filtering.