# TA-RRT*: Adaptive Sampling-Based Path Planning Using Terrain Analysis
## 4 Experiment
### Experiment in Dynamic Scenarios
Similarly, in Scenario #2, characterized by a higher density of obstacles, TA-RRT* demonstrates its adaptability by achieving a competitive replan path cost (10.0959), showcasing its robustness and adaptability even in challenging and densely populated environments. These results confirm the effectiveness of TA-RRT* in handling dynamic scenarios, generating efficient and collision-free paths regardless of obstacle density. Its performance across both scenarios underscores its potential for real-world applications in dynamic and complex environments. Figure 12: Examples of path replanning results in dynamic scenarios #1. (**a**) RRT*. (**b**) Informed-RRT*. (**c**) RRT*-Smart. (**d**) TA-RRT*. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Scenario \#1**} & \multicolumn{4}{c}{**Scenario \#2**} \\ \cline{2-10}  & \multicolumn{2}{c}{**Init. Path Length**} & \multicolumn{2}{c}{**Replan Path Length**} & \multicolumn{2}{c}{**Init. Path Length**} & \multicolumn{2}{c}{**Replan Path Length**} \\ \cline{2-10}  & **Average** & **STD** & **Average** & **STD** & **Average** & **STD** & **Average** & **STD** \\ \hline M1 & 11.5397 & 0.0295 & 10.6123 & 0.6111 & 13.1168 & 0.0940 & 10.4506 & 0.6009 \\ \hline M2 & 11.4843 & 0.0075 & 10.3232 & 0.6428 & 13.1249 & 0.1012 & 10.3599 & 0.6791 \\ \hline M3 & 12.1599 & 0.4970 & 10.4106 & 0.3761 & 13.6725 & 0.3078 & 10.0665 & 0.4411 \\ \hline M4 & 11.5280 & 0.0125 & 9.2218 & 0.7141 & 13.1140 & 0.0766 & 10.0959 & 0.6682 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Experimental results of path replanning in dynamic scenarios. Table 6 presents the number of nodes generated during path planning and replanning across two dynamic scenarios. The proposed TA-RRT* algorithm tends to generate a relatively higher number of nodes compared to other algorithms. This allows for the consideration of a wider range of possible paths, thereby facilitating the identification of more optimized trajectories. In Scenario #1, where obstacles are sparsely distributed, TA-RRT* generates an average of 1896.65 nodes during replanning. Compared to other algorithms, this demonstrates a more extensive exploration capability, leading to more effective path discovery. This suggests that TA-RRT* utilizes the search space more efficiently, reducing unnecessary exploration while still identifying optimal or near-optimal paths. Furthermore, in Scenario #2, which is characterized by a higher obstacle density, TA-RRT* generates an average of 1590.13 nodes, demonstrating its ability to explore the environment more effectively than other approaches and to consider a wider range of possible trajectories. This indicates that the algorithm maintains its capability to construct stable paths even in highly complex environments. These findings confirm that TA-RRT* can maintain robust performance in dynamic environments with varying obstacle densities, highlighting its potential for practical applications in autonomous navigation and robotic path planning. Figure 13: Examples of path replanning results in a dynamic scenario #2. (**a**) RRT*. (**b**) Informed-RRT*. (**c**) RRT*-Smart. (**d**) TA-RRT*.