# Intention Recognition for Multiple AUVs in a Collaborative Search Mission
###### Abstract  
This paper addresses the challenges of intent recognition in collaborative Autonomous Underwater Vehicle (AUV) search missions, where multiple AUVs must coordinate effectively despite environmental uncertainties and communication limitations. We propose a consensus-based intent recognition (CBIR) method grounded in the Belief-Desire-Intention (BDI) framework. The CBIR approach incorporates fuzzy inference and deep learning techniques to predict AUV intentions with minimal data exchange, improving the robustness and efficiency of collaborative decision making. The system uses a behavior modeling phase to map state features to actions and a deep learning-based intent inference phase, leveraging a residual convolutional neural network (ResCNN) for accurate intent prediction. The experimental results demonstrate that the proposed ResCNN network improves intent recognition accuracy, enhances the efficiency of collaborative search missions, and increases the success rate. autonomous underwater vehicle (AUV); collaborative search; intent recognition; residual convolutional neural network; fuzzy inference; underwater communication [1]  
observable behaviors and underlying decision-making processes. In real-world scenarios, observed information is often incomplete or noisy. Intent recognition leverages the integration of multi-source data, prior knowledge, and environmental context to reconstruct genuine intents from limited observations. Intent recognition has found widespread applications in various domains [5; 6], including human behavior prediction [7; 8], vehicle lane change intent detection [9; 10], aerial combat target intent prediction [11; 12], and question-answering systems [13; 14], supported by relatively mature methodologies. Currently, intent recognition approaches can be broadly categorized into two main types: model-based methods and data-driven methods. Model-based intent recognition methods rely on predefined frameworks combined with adaptive parameter adjustments to construct deterministic models. These methods have made significant advancements and are increasingly reaching maturity. Prominent approaches in this category include template matching, expert systems, decision trees, and Bayesian networks. These methods excel in scenarios where domain knowledge is well-understood and can be explicitly modeled. Data-driven intent recognition methods, on the other hand, leverage neural networks and deep learning techniques to learn latent patterns directly from data, without the need for prior assumptions about prototype models. The rapid advancements in data collection and computational capabilities have enabled the proliferation of data-driven algorithms in recent years. Key methods in this category include artificial neural networks, convolutional neural networks (CNNs), and recurrent neural networks (RNNs), as well as cutting-edge deep learning frameworks such as transformers. While model-based methods benefit from explainability and reliability, particularly in structured environments, data-driven approaches demonstrate superior adaptability and generalization in complex and dynamic scenarios. The integration of these two paradigms, combining the robustness of model-based inference with the flexibility of data-driven learning, represents a promising direction for advancing intent recognition technologies. Although the theory of intent recognition has seen significant advancements and has reached a relatively mature stage, its application in the field of multi-agent collaboration remains underexplored and requires further development. In particular, research on intent recognition in the domain of underwater robotic collaboration is still in its infancy, with limited foundational work and significant gaps in practical implementation. The unique challenges posed by underwater environments, such as limited communication bandwidth, high latency, and the dynamic nature of oceanic conditions, further complicate the application of intent recognition techniques [15].