# Intention Recognition for Multiple AUVs in a Collaborative Search Mission
## 5 Numerical Experimental Analysis
### Comparative Analysis with Traditional Collaborative Search Methods
This is primarily because traditional methods rely on global communication and strictly follow communication constraints for path planning, making it difficult to optimize mission execution through smarter decision making. In contrast, Figure 13b shows a more efficient path distribution, where the intent of other AUVs is predicted and used to make the collaboration between AUVs more rational, thus avoiding unnecessary path overlap. This indicates that the CBIR method, through its intent prediction and inference mechanism, effectively reduces redundant paths and enhances the efficiency of path planning. As shown in Table 9, the proposed method takes 15,340.5 s to complete the mission in Scenario B. Due to the more demanding topological structure, the mission completion time increases significantly. However, the proposed method is still capable of accomplishing the collaborative search mission, demonstrating better robustness compared to traditional methods. Figure 12: The variation in the total DPP in the collaborative search process in Scene A.  
Figure 13: The trajectories of all AUVs in completing the collaborative search mission for both methods in Scenario A. (**a**) Scenario A. (**b**) CBIR method. As shown in Figure 14, despite the increasingly stringent communication topology, the proposed method still ensures superior search efficiency. As shown in Figure 15, under extreme communication topology conditions for collaborative search missions, significant differences in performance were observed between the traditional method and the intent prediction-based CBIR method in the simulation. Figure 15a shows the search path of the traditional method under sparse communication conditions. Due to limited communication, AUVs are unable to exchange information in real-time, leading to a failure in collaboration. Some AUVs fall into a passive state after losing communication, unable to continue effectively participating in the mission, which results in team separation and ultimately failure of the search mission. The distance of 1.58 km indicated by the arrow exceeds the communication radius (1 km). Figure 15b illustrates the search path using the intent prediction-based CBIR method. Under the same extreme communication conditions, despite long periods without communication between AUVs, the CBIR method still enables global decision making based on local information through intent prediction and inference mechanisms. Each AUV is able to infer the intent of the entire team and dynamically adjust its own path, effectively avoiding the mission failure caused by communication interruptions in traditional methods. As a result, the collaborative search mission was successfully completed. Figure 14: The variation in the total DPP in the collaborative search process in Scene B.  
Figure 15: The trajectories of all AUVs in completing the collaborative search mission for both methods in Scenario B. (**a**) Scenario A. The distance of 1.58 km indicated by the arrow exceeds the communication radius. (**b**) CBIR method. \begin{table}
\begin{tabular}{l c c} \hline \hline
**Method** & **Without IR** & **Proposed** \\ \hline Arrival Time (s) & inf & 15,340.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Time for all AUVs to reach the positioning point in Scenario B. From the analysis, it is evident that the proposed method excels in adaptability and robustness under extreme communication conditions. By introducing a decision consensus-based intent recognition mechanism, this method effectively reduces dependence on global communication and significantly enhances the dynamic adjustment capabilities of multi-AUV collaborative search missions. In conclusion, the proposed method demonstrates superior search performance in complex environments and provides an efficient solution for multi-AUV collaborative search missions.