In the motion control of AUVs, especially those driven by multiple thrusters, the thruster misalignment and thrust loss cause the actual force and moment applied to the AUV to deviate from that desired, making accurate and fast motion control difficult. This paper proposes a sliding mode control method with dual-observer estimation for the AUV 3D motion control problem in the presence of thruster misalignment uncertainty and thrust loss uncertainty. Firstly, this paper considers the force and moment deviation as disturbances that vary with the controller output, and proposes the TD disturbance observer to address the problem of deviation caused by uncertainty in thruster misalignment. Secondly, this paper introduces the dynamics equation of thrust loss and designs the gain disturbance observer to estimate the thrust loss uncertainty during AUV navigation. The designed controller, verified by simulation and field tests, ensures that the AUV maintains better motion control despite thruster misalignment and thrust loss.

Broad waters, harbor waters, and waterway waters make up more than 90% of autonomous underwater vehicles (AUV) navigation area, and each of them has its typical environmental characteristics. In this paper, a three-layer AUV motion planning architecture was designed to improve the planning logic of an AUV when completing complex underwater tasks. The AUV motion planning ability was trained by the improved deep deterministic policy gradient (DDPG) combined with the experience pool of classification. Compared with the traditional DDPG algorithm, the proposed algorithm is more efficient. Using the strategy obtained from the training and the motion planning architecture proposed in the paper, the tasks of AUVs searching in broad waters, crossing in waterway waters and patrolling in harbor waters were realized in the simulation experiment. The reliability of the planning system was verified in field tests.

Sonar image is the main way for underwater vehicles to obtain environmental information. The task of target detection in sonar images can distinguish multi-class targets in real time and accurately locate them, providing perception information for the decision-making system of underwater vehicles. However, there are many challenges in sonar image target detection, such as many kinds of sonar, complex and serious noise interference in images, and less datasets. This paper proposes a sonar image target detection method based on Dual Path Vision Transformer Network (DP-VIT) to accurately detect targets in forward-look sonar and side-scan sonar. DP-ViT increases receptive field by adding multi-scale to patch embedding enhances learning ability of model feature extraction by using Dual Path Transformer Block, then introduces Conv-Attention to reduce model training parameters, and finally uses Generalized Focal Loss to solve the problem of imbalance between positive and negative samples. The experimental results show that the performance of this sonar target detection method is superior to other mainstream methods on both forward-look sonar dataset and side-scan sonar dataset, and it can also maintain good performance in the case of adding noise.