Ocean exploration requires effective collaboration between the unmanned surface vehicle (USV) and autonomous underwater vehicles (AUVs). We propose UACOF, a USV-AUV collaboration framework that enhances multi-AUV performance under extreme sea conditions. The framework includes high-precision multi-AUV location via USV path planning with Fisher information matrix optimization and reinforcement learning training for cooperative tasks. Experimental results show UACOF's superior feasibility, performance, coordination and robustness in extreme conditions.

Since seamless tracking of the underwater target is crucial for various underwater applications, we propose a fusion algorithm combining deep learning and reinforcement learning for multi-autonomous underwater vehicles (AUVs) to seamlessly track the underwater target. The framework of our proposed fusion algorithm consists of two stages. In the first stage, we propose an underwater target localization method based on convolutional neural network (CNN) that relies on shaft-rate electric fields, in which the data collected by underwater sensors is utilized to train CNN to achieve accurate target localization. In the second stage, we innovatively propose a multi-agent soft actor-critic (MASAC) reinforcement learning algorithm based on centralized training with decentralized execution, in which appropriate reward functions are designed to encourage multiple AUVs to cooperate in seamlessly tracking the target in unknown environments while avoiding obstacles. Simulation results show that the proposed fusion algorithm has excellent performance, while the real-time target localization accuracy is 97.8%, and AUVs can carry out seamlessly cooperative tracking of the target in unknown environment.