# Intention Recognition for Multiple AUVs in a Collaborative Search Mission
## 4 CBIR Method in the AUV Collaborative Search Mission
### Intent Recognition Model Based on Residual Convolutional Neural Networks  
Intent recognition in underwater collaborative search missions is essentially a classification problem. We referred to the residual block design concept proposed in [46] and based on this idea, constructed the intent recognition network in this work. This architecture introduces skip connections, which allow deep neural networks (DNNs) to maintain effective gradient flow in deeper layers, thus avoiding the vanishing gradient problem during the training process [47]. This structure significantly improves the training efficiency and enhances the model's performance. The network structure is shown in Figure 5, consisting of multiple layers of residual blocks, which effectively learn the features of the input data and infer the intent of the AUV. The network architecture consists of an initial convolutional layer (Conv) followed by several residual blocks, each containing multiple convolutional layers. Specifically, our network features a sequence of residual blocks, with each block containing two convolutional layers. The output of the last residual block is then passed through a fully connected layer (Dense), followed by a softmax activation function. This activation function is used because the classes are not mutually exclusive; meaning, multiple classes may be predicted simultaneously. Each convolutional layer's output is rescaled using batch normalization (BN) and is then passed through a rectified linear activation unit (ReLU). Additionally, dropout is applied after the nonlinearity to prevent overfitting. The network consists of a series of convolutional and residual blocks, designed for 1D data processing. It begins with an initial convolutional layer, which applies a 1D convolution with 16 filters, followed by batch normalization, a ReLU activation function,  
Figure 5: The network structure used for AUV intent recognition in a collaborative search mission. and max pooling with a stride of 2. This first block reduces the spatial dimensions of the input data while extracting low-level features. Following the initial block, there are four residual blocks, each constructed using the optimized residual network module. These blocks progressively increase the number of filters from 16 to 256, allowing the network to capture increasingly complex patterns as the depth increases. Each residual block contains skip connections to help the network learn deeper features without suffering from the vanishing gradient problem. After passing through the residual blocks, the network uses an adaptive average pooling layer to reduce the spatial dimension to a fixed size of 1, regardless of the input length. This is followed by a flattening operation to prepare the output for the final fully connected layer. The output from the flattening operation is passed through a fully connected layer with 256 input units, which outputs a prediction for categories. Overall, this architecture effectively balances feature extraction and complexity, utilizing residual connections to improve learning capacity while keeping the model relatively simple and efficient for classification missions. Compared to traditional convolutional neural networks, the introduction of ResCNN enables the model to learn complex patterns more flexibly and achieve better generalization with smaller training samples. In AUV intent recognition, the combination of ResCNN and CNN not only captures the deep relationships between AUV behaviors and states but also improves the recognition accuracy and robustness in complex environments. The training process of the ResCNN for intent recognition in AUV collaborative search missions involves several key steps. First, the training dataset is preprocessed using a data loader to convert state features and intent labels into PyTorch tensors. The ResCNN model is constructed with convolutional layers and residual blocks to maintain gradient flow and prevent vanishing gradients.