# Path Planning of Intelligent Mobile Robots with an Improved RRT Algorithm
## 3 Experimental Validation
### Multi-Map Environment Path Planning Simulation Experiment
(**a**) RRT-1; (**b**) RRT-2; (**c**) RRT-3; (**d**) improvement of the RRT-KF algorithm. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Algorithm**} & **Average Path** & **Average** & **Average** & **Average** & **Average** & **Whether It Is** & **Channel** \\  & **Length** & **Time** & **Collision** & **Sampling** & **Active Points** & **the Best Path** & **No.1/2/3** \\ \hline \multirow{2}{*}{RRT} & 40.310 & 0.156 & 23 & 177 & 36 & Yes & 2 \\  & 44.297 & 0.204 & 31 & 192 & 41 & No & 1 \\  & 45.418 & 0.317 & 36 & 227 & 44 & Yes & 3 \\ \hline Improved algorithm & 31.818 & 0.071 & 2 & 48 & 33 & No & 2 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of multi-channel map simulation data. Figure 11a shows that the traditional RRT algorithm can find the path from the starting point to the target point, but the node search is more complex and multiple collisions occur. In addition to this, the traditional RRT algorithm has unreasonable path planning as shown in Figure 11b, where the black rectangle is selected to pass in the map. In contrast, the improved algorithm in this paper obtains smooth path curves with fewer collisions, greatly reducing redundant nodes and faster path search. As shown in Figure 12, the bar graph of the path length comparison between 40 groups of experiments before and after optimization, it can be seen that the optimized paths are shorter than the original paths, and the optimized algorithm is more stable in path planning, which reflects the robustness of the algorithm, and the optimized average paths are reduced by 16.64% compared with the original average path lengths, which reduces the expansion of the random tree nodes' searches in the irrelevant regions, thus reducing the time to find the initial path and the initial path lengths. This reduces the search expansion of random tree nodes in irrelevant regions, thus reducing the time to find the initial path and the length of the initial path. In addition to the above four 2D environments, in order to verify the effectiveness of the improved algorithm in 3D environments, three sets of experiments are set up to compare the path planning parameters of the improved algorithm of this paper with those of the traditional RRT algorithm and the RRT-Greedy algorithm in the same map environments, and the simulation results are shown in Figure 13.  
Figure 11: Simulation results of path planning for complex environments. 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 black rectangles represent obstacles, which include multiple uncertain paths, and the red solid line represents the final path from the starting point to the target point. (**a**) RRT-pathway; (**b**) RRT-Collision; (**c**) improvement of the RRT-KF algorithm. Figure 12: Comparison of path lengths in complex environments. From Figure 13a-c, it can be seen that the traditional RRT algorithm for path planning in 3D environment still generates many redundant sub-nodes due to its randomness, while the RRT-Greedy algorithm reduces the planning time and the number of sampling points to a certain extent. From the data in Table 5, it can be seen that the improved algorithm in this paper, compared with the traditional RRT algorithm and RRT-Greedy algorithm, has a significant reduction in the average path planning length by 5.20% and 0.46%, respectively; the average path planning time is reduced by 68.44% and 45.79%, respectively; the average number of sampling points is greatly reduced by 56.50% and 20.49%, respectively. While the average effective number of path points is almost the same, the effective rate is increased to show the accuracy of the algorithm's decision-making in the path planning process, and the above three sets of experiments illustrate the feasibility of this paper's improved algorithm in the three-dimensional map environment.