# Intention Recognition for Multiple AUVs in a Collaborative Search Mission
## 4 CBIR Method in the AUV Collaborative Search Mission  
To address the challenges faced by underwater robots in collaborative search missions, we propose a method called consensus-based intent recognition (CBIR), shown in Figure 1. We first unified the AUV swarm decision-making process based on the BDI framework. To establish the mapping between behavioral states and intentions, we introduced the concept of \"landmarks\", standardizing the intention types throughout the swarm decision-making process. These intention types include optimizing communication, enhancing detection, and rapidly approaching the target. Building on our previous work [44], we identified the key information influencing AUV decision-making: distance to the positioning point (DPP), success rate of received data (PRR), distance to the PNCT (DPN), number of companions (NoCs), detection error (DE), and variance of detection error (VDE).  
These features were used as inputs for the fuzzy inference model, with the three intention types as outputs. Using a fuzzy inference method, we constructed a mapping between this information and the intended actions, achieving consensus among the AUV  
Figure 1: The CBIR framework and intention recognition process.  
swarm. This approach ensures that when different AUVs receive the same or similar state information, they make consistent decisions.  
Once the mapping relationship is established, intent recognition for other AUVs can be achieved by classifying this information. However, due to the large delays and high packet loss characteristics of underwater acoustic communication, certain key information with significant temporal fluctuations, such as PRR, DE, and VDE, could negatively impact classification accuracy. Therefore, we replaced these variables with alternative features that exhibit less temporal variability but still reflect the necessary characteristics. The final input features for the classification model include the DPP, DPN, relative direction to the positioning point (RDP), relative direction to the PNCT (RDPN), number of companions (NoCs), and the average detection error (ADE). The outputs of the classification model remain consistent with the three intention types: communication priority, detection priority, and positioning priority.  
In the collaborative search process of AUVs, information flow begins with collecting key state data from other AUVs, including parameters such as DPP, DPN, RDP, RDPN, NoCs, and ADE. These data are processed by a pre-trained ResCNN to predict the intent of other AUVs. Using both predicted intent and data received through underwater acoustic communication, the system computes the future state of other AUVs. The fuzzy inference model uses the following information: DPP, PRR, DPN, NoC, DE, and VDE to determine the local AUV's intent, ensuring consensus within the swarm. Finally, the identified intent guides the path planning process, enabling the AUV to make adaptive decisions and contribute effectively to the collaborative search mission. The overall process is illustrated in Figure 2.  
In summary, by combining fuzzy inference and deep learning, and utilizing landmark consensus, the CBIR method effectively reduces communication overhead, enhancing the robustness and scalability of intent recognition in collaborative search missions. This provides an efficient and practical solution for AUVs to perform collaborative missions and achieve common objectives in complex underwater environments.