Autonomous underwater vehicles (AUVs) may deviate from their predetermined trajectory in underwater currents due to the complex effects of hydrodynamics on their maneuverability. Model-based control methods are commonly employed to address this problem, but they suffer from issues related to the time-variability of parameters and the inaccuracy of mathematical models. To improve these, a meta-learning and self-adaptation hybrid approach is proposed in this paper to enable an underwater robot to adapt to ocean currents. Instead of using a traditional complex mathematical model, a deep neural network (DNN) serving as the basis function is trained to learn a high-order hydrodynamic model offline; then, a set of linear coefficients is adjusted dynamically by an adaptive law online. By conjoining these two strategies for real-time thrust compensation, the proposed method leverages the potent representational capacity of DNN along with the rapid response of adaptive control. This combination achieves a significant enhancement in tracking performance compared to alternative controllers, as observed in simulations. These findings substantiate that the AUV can adeptly adapt to new speeds of ocean currents.

This study addressed the autonomous planning of three-dimensional (3D) underwater inspection paths for autonomous underwater vehicles (AUVs) in marine ranching by integrating differential evolution and particle swarm optimization (PSO) algorithms. First, a modified PSO algorithm incorporating swap operators, mutation, and crossover strategies was employed to enable autonomous obstacle avoidance during the inspection of offshore net cages in fish farms situated within a 3D marine environment. This approach addresses the problem of planning full-traversal paths for multiple inspection points. Second, the performance of the proposed algorithm was assessed through comparative tests with other algorithms. The proposed algorithm demonstrated significant improvements in convergence speed, accuracy, and stability under complex scenarios involving multiple optima and intense oscillations. To validate the superiority and overall planning proficiency of the modified method, an experimental setup comprising of two distinct 3D marine cage environments with a series of checkpoints was utilized. The experimental results demonstrated the ability of the proposed algorithm to generate an optimal path while traversing all inspection points of fish farm offshore net cages. By ensuring the safety of AUVs and closely adhering to the surfaces of offshore net cages during the inspection process, the algorithm exhibits remarkable adaptability to specific application scenarios, effectively mitigating concerns related to local optima and premature convergence.

Underwater simultaneous localization and mapping (SLAM) constitutes a critical foundation for marine vehicle navigation. However, underwater SLAM faces challenges such as high motion distortion and low matching efficiency. In response to this, we introduce a visual-inertial odometry (VIO) and sonar fused SLAM framework with enhanced corner feature matching. Initially, we employ VIO to remove the motion distortion. Additionally, we design a localized corner feature matching mechanism to enhance iterative closest point (ICP) SLAM, aiming to increase both the matching frequency and precision. Experimental results demonstrate that our approach outperforms the compared methods in terms of performance.