# Multi-Task Agent Hybrid Control in Sparse Maps and Complex Environmental Conditions
## 3 Dynamic Environment Target Identification and Localization (DETIL)
### Simulation Validation and Performance Analysis  
The experimental setup utilized in this study includes a machine equipped with an Intel Core i9-13900 KF processor, an NVIDIA GeForce RTX 4090 graphics processing unit, and 64 GB of memory. Within the Webots simulation environment, 4 sets of 50 m \(\times\) 50 m diverse lunar surface maps were used to simulate complex environmental conditions. The corresponding elevation maps, devoid of obstacles and represented as 500 \(\times\) 500 matrices, served as prior conditions for the intelligent agent's path planning. De  
Figure 6: Overall flowchart of the DETIL algorithm for interest target identification and localization. tailed experimental data for each pathfinding algorithm are presented in Table 1, with each data point representing the average value obtained from planning paths across the 4 sets of maps. The A* path planning algorithm employing the evaluation function proposed in this research reduced the average number of expanded nodes by 45% compared to the BFS algorithm and decreased the maximum elevation difference by 55% and the total maximum path elevation difference by 68% compared to the binary map combined with Euclidean distance A* algorithm. With the addition of the adaptive heuristic function, the average number of expanded nodes decreased by 6%. From Figure 7a,b, it is evident that the paths obtained by traditional path planning algorithms tend to closely follow the areas considered to be obstacles. As can be seen from Figure 7c,d, the inclusion of the obstacle distance cost function enabled the algorithm to effectively avoid obstacles and their surrounding hazardous areas, seeking out flatter routes. The data indicate that the adaptive heuristic function A* path planning algorithm enhances the safety and robustness of path planning in complex environments using only sparse maps. After completing the path planning, the intelligent agent carries out movement and interest target discovery tasks in the simulation environment. Each map is tested 10 times, resulting in a total of 40 simulation experiments. The DETIL algorithm successfully identified all unknown obstacles and completed obstacle avoidance in all 40 simulation experiments. On average, the obstacle avoidance judgment time was 28 ms in each simulation experiment. The algorithm can effectively resist the interference of rugged terrain and correctly identify obstacles that may impact the intelligent agent's progress. In the interest target discovery part of the simulation experiments, a simple color recognition detection head was used to locate interest targets in two-dimensional images. After completing the movement tasks, the DETIL algorithm was used for identification and localization. An interest target was considered discovered if its located position was less than 2 m from the actual center. Detailed experimental data are shown in Table 2. With the same input, the spatiotemporal dimension clustering based on DBSCAN increased the average number of target discoveries by 152% compared to the conventional DBSCAN \begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Algorithm Name** & **Average Path** & **Average Calculation** & **Average Expanded Time** & **Average Maximum Nodes** & **Average Maximum Elevation Difference (dm)** & **Average Total Change (dm)** & **Maximum Total Change (dm)** \\ \hline Dijkstra & 310.25 & 1.32 & 17,624.25 & 2.31 & 2.78 & 151.28 & 227.98 \\ \hline BFS & 310.25 & 8.35 & 134,185.00 & 4.01 & 9.60 & 156.24 & 227.14 \\ \hline A* (Binary Map + Euclidean Distance) & 313.75 & 0.04 & 1297.50 & 2.35 & 3.51 & 136.78 & 223.52 \\ \hline A* (Elevation Map + Adaptive Heuristic Function ) & 339.25 & 9.10 & 73,970.50 & 0.79 & 1.58 & 48.29 & 71.15 \\ \hline A* (Ours) & & & & & & & \\ \hline A* (Elevation Map + Heuristic Function + O(n)) (Ours) & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of simulation results among various path planning algorithms.