Aiming at the problem of path planning for autonomous underwater vehicle (AUV) to cope with the influence of obstacles and eddies in complex marine environments, a path planning method based on an improved salp swarm algorithm (ISSA) is proposed. Firstly, the motion model of the AUV and eddy current model are constructed, including the relationship between position, velocity, attitude, and control inputs. Secondly, the improved SSA is proposed, which introduces the Levy flight strategy to enhance the algorithm’s optimization seeking ability and adds a nonlinear convergence factor to enhance the convergence ability of the algorithm. The stability and robustness of the improved algorithm are verified by test functions. Finally, the ISSA is applied to AUV path planning, which optimizes the AUV travel distance, improves the search efficiency and accuracy, and avoids the local optimum of the algorithm. The ISSA enhances the adaptive ability and robustness of the algorithm by introducing a dynamic adjustment strategy and feedback mechanism. Experimental verification is carried out using a simulated marine environment. The results show that the ISSA is better than the traditional algorithm in terms of path length as well as algorithm stability, and can effectively improve the navigation performance of AUV.

Unmanned surface vehicles (USVs) often operate in real-world environments with long voyage distances and complex routes. The use of a single-grid map model presents challenges, such as the high computational costs for high-resolution maps and loss of environmental information for low-resolution maps. This article proposes an environmental modeling method using a hybrid map that combines topology units and grids. The approach involves calibrating key nodes based on the watershed skeleton line, constructing a topology map using these nodes, decomposing the original map into unit maps, converting each unit map into a grid map, and creating a hybrid map environment model that comprises topology maps, unit map sets, and grid map sets. Then, the article introduces an improved genetic algorithm, called Genetic Algorithm with Feedback (FGA), to address path planning in hybrid maps. Experimental results demonstrate that FGA has better computational efficiency than other algorithms in similar experimental environments. In hybrid maps, path planning with FGA reduces the path lengths and time consumption, and the paths are more logical, smooth, and continuous. These findings contribute to enhancing the quality of path planning and the practical value of USVs.

An improved multi-objective particle swarm optimization algorithm is combined with a machine learning classifier to meet the needs of underwater manned/unmanned cooperative warfare architecture evaluation. Firstly, based on the traditional Cauchy variation method, the particles in the population are disturbed in a dynamic way so that the particles trapped in the local optimal can jump out of the local optimal, and the convergence performance of the particle swarm optimization is improved. Secondly, the accuracy of the index set is analyzed based on the CART decision tree algorithm and the IWRF algorithm. A screening method of key indexes with fewer evaluation indexes and high evaluation accuracy is developed to solve the problem of a large number of evaluation indexes and unclear correlation of the underwater combat system. Through simulation, the extraction results of key indicators were verified, and the reliability coefficient of the final simulation experiment was 0.93, which can be considered as high reliability and effectiveness of the key indicators extracted in this study. By combining multi-objective optimization with machine learning and weighing evaluation efficiency and accuracy, a high-precision and rapid evaluation of a few indicators is achieved, which provides support for establishing an evaluation model of SoS architecture for underwater manned/unmanned cooperative operations. This research result can provide inspiration for the evaluation and evaluation of the system in order to analyze the accuracy of indicators and the evaluation effect and carry out research by simulating the actual system with tools with a high simulation degree.

We present a novel approach for achieving high-precision trajectory tracking control in an unmanned surface vehicle (USV) through utilization of receding horizon reinforcement learning (RHRL). The control architecture for the USV involves a composite of feedforward and feedback components. The feedforward control component is derived directly from the curvature of the reference path and the dynamic model. Feedback control is acquired through application of the RHRL algorithm, effectively addressing the problem of achieving optimal tracking control. The methodology introduced in this paper synergizes with the rolling time domain optimization mechanism, converting the perpetual time domain optimal control predicament into a succession of finite time domain control problems amenable to resolution. In contrast to Lyapunov model predictive control (LMPC) and sliding mode control (SMC), our proposed method employs the RHRL controller, which yields an explicit state feedback control law. This characteristic endows the controller with the dual capabilities of direct offline and online learning deployment. Within each prediction time domain, we employ a time-independent executive–evaluator network structure to glean insights into the optimal value function and control strategy. Furthermore, we substantiate the convergence of the RHRL algorithm in each prediction time domain through rigorous theoretical proof, with concurrent analysis to verify the stability of the closed-loop system. To conclude, USV trajectory control tests are carried out within a simulated environment.

This paper addresses the cooperative formation control problem for a heterogeneous unmanned system composed of Unmanned Surface Vehicles (USVs) and Autonomous Underwater Vehicles (AUVs) under coexisting constraints of actuator faults, time-varying communication topology, and communication delay. First, a unified dynamic model is established under the Euler–Lagrange framework. Building on this, a novel distributed adaptive fault-tolerant control (DAFTC) framework is proposed. This framework integrates a Dynamic Event-Triggered Mechanism (DETM) to address communication bandwidth limitations, alongside an adaptive fault-tolerant strategy to enhance system robustness. The novelty lies in the cohesive integration of DETM for communication efficiency and adaptive laws for online fault compensation (both loss of effectiveness and bias), while rigorously handling communication delays via Lyapunov–Krasovskii analysis. It is proven via Lyapunov stability analysis that the proposed control protocol ensures all signals in the closed-loop system remain semi-globally uniformly ultimately bounded, with the formation tracking error converging to an adjustable compact set. Simulation results demonstrate the framework’s effectiveness. Compared to periodic communication (0.1 s interval), the proposed DETM reduces the communication load by over 99.6%. Even when subjected to a 25% effectiveness fault and a 5 Nm bias fault, the root-mean-square (RMS) tracking error is maintained below 0.15 m, validating the system’s high performance and robustness.

The deep-sea convergence zone (CZ) is a critical phenomenon for long-range underwater acoustic propagation. Accurate prediction of its distance, width, and gain is essential for enhancing sonar detection performance. However, conventional ray-tracing models, which assume vertically stratified sound speed profiles (SSPs), fail to account for horizontal sound speed gradients in the mixed layer, leading to significant prediction errors. To address this, we propose a novel ray-tracing model that incorporates horizontally inhomogeneous SSPs in the mixed layer. Our approach combines empirical orthogonal function (EOF) decomposition with the Del Grosso sound speed formula to construct a continuous 3D sound speed field. We further derive a modified ray equation including horizontal gradient terms and solve it using a fourth-order Runge–Kutta method. Simulation and experimental validation in the South China Sea demonstrate that our model reduces the prediction error for the first CZ distance by 2.26%, width by 2.66%, and gain deviation by 5.85% compared to the Bellhop model. These results confirm the effectiveness of our method in improving CZ parameter prediction accuracy.