For the design and verification of a motion control algorithm for unmanned surface vehicles, a simulation system is developed based on VRX and Simulink. Firstly, considering the effect of wind, a dynamic model of the USV with podded propellers is established. Secondly, combined with speed control, three control modes are considered, including yaw rate control, heading control, and path-following control, and speed, heading, yaw rate, and path guidance controllers are designed. Then, a real-time simulation system is developed based on the Virtual RobotX (VRX) environment and the Simulink ROS2 toolbox. Finally, motion control simulation experiments under three control modes and a path-following water tank experiment are carried out. The designed simulation system can simulate the motion of USVs and different environmental elements, such as wind, intuitively and realistically. In simulation experiments, the designed controllers can make the USV follow commands quickly and accurately under three control modes. In the water tank experiment, the USV could stably track the desired path with a relatively small tracking error. Therefore, the effectiveness of the simulation system is strongly confirmed through simulation experiments and the water tank experiment. The simulation system will be expanded in the future for more research on target recognition, path planning, and other aspects of USVs.

The dispersion effect of seawater can cause the envelop distortion of underwater eLoran signals, which causes the envelope-to-cycle difference (ECD) to exceed the standard range. Furthermore, it results in incorrect cycle identification and significant positioning errors. However, few studies have focused on the distortion caused by the dispersion effect. In this study, we propose an accurate underwater eLoran ECD compensation method based on a variable step size least mean square (VSS-LMS) algorithm. First, a systematic modeling approach was employed to investigate the impact of dispersion effects on Loran signals. Second, the VSS-LMS algorithm was introduced to update the filter weight vector in response to discrepancies in the input signal. Finally, the input signal was subjected to an adaptive transversal filtering process, resulting in an output signal that adhered to the specifications of the ECD standard. The efficacy and superiority of the proposed algorithm were demonstrated by experimentation and simulation. When the depth of seawater exceeds 2 m, the ECD value of the original eLoran signal exceeds the standard range, precluding the possibility of cycle identification. However, when the depth of seawater reaches 4 m, the ECD of the signal compensated by the proposed algorithm adaptively compensates for the normal range, thereby enabling the accurate recognition of cycles.

To meet the development requirements of the path planning unit for unmanned underwater vehicles (UUVs), research is conducted on UUV 3D obstacle avoidance path planning algorithms and software design. Firstly, aiming at the problem of underwater 3D obstacle avoidance path planning for UUVs, a global path planning algorithm based on the improved D* Lite is designed, and a local path planning algorithm combining the 3D obstacle avoidance strategy and the improved artificial potential field (APF) algorithm is designed. Secondly, based on the above path planning algorithms, a UUV 3D obstacle avoidance path planning software is developed under the Robot Operating System 2 (ROS2) framework and deployed on an Orange Pi 5B. To test the algorithms and the developed software, a UUV autonomous navigation hardware-in-the-loop (HIL) simulation system is constructed. Finally, based on this system, three types of HIL simulation experiments are conducted, including global path planning, local path planning, and comprehensive obstacle avoidance path planning. The simulation experiments show that the improved D* Lite-APF algorithm has better comprehensive performance than the traditional D* Lite-APF algorithm; the path planning software can guide the UUV to reach the goal point safely and runs stably and reliably. The designed UUV 3D obstacle avoidance path planning algorithm and software exhibit good obstacle avoidance performance and can be applied to the rapid development of actual UUV path planning units.

To address the control coupling challenges arising from task heterogeneity of unmanned surface vehicle (USV) formation, a distributed hybrid deep reinforcement learning (HDRL) framework is proposed. The framework decomposes the formation task into two subtasks: leader path planning using the single-agent deep reinforcement learning (SADRL) algorithm and follower formation tracking using the multi-agent deep reinforcement learning (MADRL) algorithm. By embedding the physical constraints of the real Otter USV into the training loop, the policy network outputs are mapped to propeller revolutions that conform to its dynamic characteristics. To optimize control performance, a dynamic gating mechanism triggered by formation position error is developed to mitigate multi-objective interference through temporal task scheduling. Concurrently, a mirror mapping mechanism leveraging the physical symmetry of the formation is designed to achieve policy sharing and data augmentation. Furthermore, the desired velocity calculated based on rigid-body kinematics is used to achieve kinematic-compensated formation tracking. The simulation results indicate that, compared to the SADRL algorithm, the planning success rate of HDRL is improved by 44.59%. Furthermore, compared to the MADRL algorithm, the integrated tracking performance is enhanced by 21.79–39.64%.

The Loran-C system employs the spherical hyperbola positioning (SHP) method. However, SHP has three drawbacks in inland regions: first, approximating the Earth’s ellipsoid as a sphere introduces positioning errors; second, hyperbola positioning inherently suffers from a high geometric dilution of precision (GDOP) value; third, it is not easy to simultaneously receive long-wave signals from an entire chain of stations under complex propagation paths, which, to some extent, limits the application and development of the Loran-C system in inland areas. This paper addresses the limitations of the SHP algorithm and introduces the ellipsoidal pseudorange positioning (EPP) method, which eliminates the need to approximate the Earth’s ellipsoid as a sphere. This pseudorange positioning algorithm reduces the GDOP value, enabling navigation and positioning with signals from just three stations, thereby breaking through the restriction of requiring signals from a single chain. Simulation analyses were conducted for various Loran-C chains in China. Due to differences in the geometric layout of the chains, the EPP algorithm improved the positioning coverage area by 129.1% to 284.6% compared to the SHP algorithm. In field test data from the Maoming region of Guangdong Province, China (a typical inland mountainous environment), the EPP algorithm significantly reduced the root mean square error (RMSE), from 417.2 m with the SHP algorithm to 43.1 m, representing an improvement of 89.7%. Both the simulation and experimental results demonstrate that the EPP method effectively addresses errors in Earth ellipsoid modeling, significantly reduces the GDOP, and substantially improves the positioning accuracy and usable area of the Loran-C system in complex inland terrain. This provides more reliable technical support for Loran-C applications in inland navigation, timing, and timing backup.

To fulfill the requirements for rapid development of high-performance AUV motion control systems, a three-dimensional (3D) path- following control system is designed and implemented based on the MOOS-IvP/ArduSub architecture. Using Lyapunov stability theory, an adaptive line-of-sight (ALOS) guidance law with variable look-ahead distances (V$\Delta$-ALOS) is designed. Furthermore, based on pole placement, kinematic controllers for yaw angle and depth, along with dynamic controllers for surge velocity, yaw rate, and pitch angle, are designed, enabling dual-mode control comprising decoupled steering/diving plane control and 3D spatial path- following control. On the established simulation platform, hardware-in-the-loop (HIL) comparative experiments are conducted for two typical 3D paths (a “straight line–turning circle–straight line” path and a sinusoidal path) under compound disturbances including ocean currents, parametric uncertainty, communication delays, and sensor errors. The experimental results demonstrate that the proposed V$\Delta$-ALOS guidance law exhibits significant advantages over the integral line-of-sight (ILOS) guidance law and two existing ALOS guidance laws in terms of dynamic performance and steady-state accuracy, with slightly lower propulsion control effort.