# Intention Recognition for Multiple AUVs in a Collaborative Search Mission
## 5 Numerical Experimental Analysis  
### Numerical Experimental Data and Environment  
The simulation experiment is based on the LP-AUV [48], as shown in Figure 6, which features a wide speed range and high load capacity, allowing it to carry various sensors for rapid response operations. Due to space limitations, detailed information on the LP-AUV mathematical model and the relevant parameters of the LP-AUV can be found in reference [48]. For a detailed modeling process of the underwater acoustic channel, please refer to our previous work [44].  
The simulation scenario is illustrated in Figure 7. During the dataset generation, the initial positions of the AUVs are randomly distributed. In the initialization phase, the communication network is ensured to be connected. We assume that initially, only one AUV (referred to as \(\text{AUV}_{1}\)) detects the target's position during the simulation. To enhance detection accuracy, collaborative detection and localization are required. Therefore, based on the target's initial position and the needs of collaborative localization, several positioning points are designed to guide the remaining AUVs to reach these points quickly. During the initial phase, the Hungarian method is used to assign each AUV a positioning point, with the primary criterion being the minimization of the total travel distance of the AUV swarm.  
Communication among the AUV swarm uses a multi-hop communication approach. In multi-hop communication, data are transmitted from one AUV to another through intermediate nodes rather than directly between the source and the destination. This method is particularly beneficial in underwater environments where acoustic communication has limited range, high latency, and high packet loss rates. The communication follows a hierarchical flooding routing protocol, ensuring efficient data dissemination while reducing redundant transmissions. During the simulation, the information exchanged between AUVs is affected by end-to-end delay and packet loss rate. For detailed calculation methods, please refer to our previous work [44]. During the collaborative search process, if any AUV loses connection, the search is considered unsuccessful. If all AUVs enter the tracking phase, the collaborative search is deemed successful.  
The number of AUVs significantly impacts the communication topology and the complexity of collaborative decision making. If the number of AUVs is too small, the problem becomes overly simplistic, failing to demonstrate the effectiveness of the decision-making process. The speed difference between the AUVs and the target also influences the success rate of the collaborative search. AUVs moving only slightly faster than the target may struggle to achieve effective positioning and tracking, while a significant speed advantage could make the mission less challenging and reduce the need for advanced intent recognition and collaborative strategies. Although the actual communication radius of underwater acoustic networks can exceed 1 km, we set a 1 km communication and detection radius in this study to simplify the problem and focus on the proposed method's core aspects. To focus on specific research questions, we set the number of AUVs to nine, with each AUV having a cruising speed of 4 knots. The target consists of a single object, which moves at a speed of 2 knots. The parameters for underwater acoustic communication are configured to give the AUVs a communication radius of 1 km, as detailed in Table 4. The detection radius was also set to 1 km.  
Figure 6: LP-AUV at the lake test site.  
Figure 7: A schematic diagram of the initial state of the collaborative search mission.  
The experimental computer system is Windows 11, with Python version is 3.8.0. The laptop was sourced from Lenovo, Beijing, China, and is equipped with NVIDIA GeForce RTX 3060 GPU. CUDA 11.0 is used for acceleration, and PyTorch 1.8.0 deep learning framework is used.