# Intention Recognition for Multiple AUVs in a Collaborative Search Mission
###### Abstract
In this study, \"landmarks\" are categorized into three types: optimizing communication, enhancing detection, and rapidly approaching the target. This classification unifies the intention types throughout the entire swarm decision-making process. In practical operations, the situational information and action sequences of the AUV swarm often constitute massive datasets. It is neither feasible nor practical to use all this information to establish a mapping between behavioral states and intentions. On one hand, underwater acoustic communication is limited and cannot support large-scale data exchange. On the other hand, only a subset of this information truly influences intentions, while most of the data are redundant. To address these challenges, we propose using a fuzzy inference method to establish the mapping between state information and AUV intentions. Specifically, by combining the three intention types mentioned above, we select key information that impacts decision making. By adjusting the fuzzy inference model, we enable it to produce corresponding intention results based on varying input data. Additionally, this approach ensures that when different AUVs receive identical or similar state information, they can make consistent decisions, achieving consensus within the swarm. We refer to this process as establishing the AUV's behavior model. In an ideal scenario where no communication packet loss or delay exists between AUVs, the intentions of other AUVs could be directly inferred using identical state features based on the behavior model. However, each AUV can only infer the behavior intentions of others based on similar, rather than identical, state features. To overcome these challenges, we propose the use of a residual convolutional neural network (ResCNN) to achieve robust and accurate intention inference for AUVs, even under the constraints of unreliable communication. The main innovations of the proposed method are summarized as follows:  
* Consensus Mechanism Based on \"Landmarks\"  
The method introduces the concept of \"landmarks\" to construct consensus among AUVs, focusing only on critical information that influences key behaviors. By leveraging the behavioral consistency of AUVs in similar or identical situations, this approach significantly reduces the data volume required for intent recognition, alleviates communication overhead in underwater acoustic channels, and enhances both the accuracy and efficiency of intent prediction. * Dual-Stage Architecture for Behavior Modeling and Intent Inference  
The proposed method adopts a dual-stage architecture comprising a behavior modeling phase and an intent inference phase. In the behavior modeling phase, fuzzy inference maps the state space to the action space, providing an initial characterization of the current intent. The intent inference phase employs a residual convolutional neural network (ResCNN) to further analyze and predict the target intent based on behavioral data. This modular design enhances the flexibility and predictive power of the system. The remainder of this paper is organized as follows: Section 2 provides a brief overview of several intent recognition methods, highlighting their strengths and limitations. Section 3 introduces the problem of intent recognition for the AUV collaborative search mission. Section 4 presents a detailed explanation of the proposed behavior modeling and intent inference methods. Section 5 conducts experiments and analyzes the performance of the proposed model. Finally, Section 6 concludes the paper with a summary of the findings and discusses potential directions for future work.