Target search for moving and invisible objects has always been considered a challenge, as the floating objects drift with the flows. This study focuses on target search by multiple autonomous underwater vehicles (AUV) and investigates a multi-agent target search method (MATSMI) for moving and invisible objects. In the MATSMI algorithm, based on the multi-agent deep deterministic policy gradient (MADDPG) method, we add spatial and temporal information to the reinforcement learning state and set up specialized rewards in conjunction with a maritime target search scenario. Additionally, we construct a simulation environment to simulate a multi-AUV search for the floating object. The simulation results show that the MATSMI method has about 20% higher search success rate and about 70 steps shorter search time than the traditional search method. In addition, the MATSMI method converges faster than the MADDPG method. This paper provides a novel and effective method for solving the maritime target search problem.

The marine environment presents a unique set of challenges for human–robot interaction. Communicating with gestures is a common way for interacting between the diver and autonomous underwater vehicles (AUVs). However, underwater gesture recognition is a challenging visual task for AUVs due to light refraction and wavelength color attenuation issues. Current gesture recognition methods classify the whole image directly or locate the hand position first and then classify the hand features. Among these purely visual approaches, textual information is largely ignored. This paper proposes a visual–textual model for underwater hand gesture recognition (VT-UHGR). The VT-UHGR model encodes the underwater diver’s image as visual features, the category text as textual features, and generates visual–textual features through multimodal interactions. We guide AUVs to use image–text matching for learning and inference. The proposed method achieves better performance than most existing purely visual methods on the dataset CADDY, demonstrating the effectiveness of using textual patterns for underwater gesture recognition.

Changes in the dissolved oxygen concentration of the ocean have important implications for marine ecosystems and global climate change. However, limited by measurement techniques, the hydrology data is not always complete. Thus, accurate prediction on marine dissolved oxygen concentration (MDOC), is a powerful supplement to the current observation data. Deep neural network is a powerful model to do the prediction, while it is usually difficult and time-consuming to tune its structure. Meanwhile, deep jointly informed neural network (DJINN) provides a user friendly method to tune the structure of neural networks. In this article, a deep learning-based model called marine deep jointly informed neural network (M-DJINN), is proposed to predict MDOC. M-DJINN improves DJINN performance via initializing the weights of neural network using a zero-mean Gaussian distribution with a variance related to the number of neurons in the neighbor layer. In M-DJINN, to get an ideal deep neural network structure, users only need to tune the tree number and max tree depth. While predicting MDOC on World Ocean Database 2013(WOD13) data with M-DJINN, the novel model proves better than DJINN on both accuracy and convergency. When the max tree depth is set to 10, the mean squared error (MSE) is reduced by 17.6% compared with DJINN. It can be concluded from the experiments that with enough training data, the performance of M-DJINN may keep improving by deepening the neural networks.