To overcome the limitations of a single unmanned surface vehicle (USV), this study investigated the swarm control algorithms of USVs. Among various swarm control methods, a leader–follower swarm control method was selected and studied. The performance of the swarm algorithm proposed in this study was verified through an actual sea area test. A USV was designed and manufactured by dividing the power and communication parts. The power system was equipped with a coulometer to monitor the battery state in real time to protect the system through a switch linked to the coulometer in the case of abnormalities in the battery and to prevent accidents. In addition, a communication system was established to process the sensor data and camera image data of the USV in real time. Consequently, the desired swarm formation was achieved through the separately constructed swarm control algorithm. Before the actual sea area test, individual performance tests of each sensor were conducted. Finally, the performance of the swarm algorithm was verified by conducting a sea area test. The performance of the controllers was confirmed to be good, and the swarm formation was confirmed to be successful.

This study focuses on faults in the thrusters of unmanned surface vehicles, which are fatal to the integrity of their missions. As for the fault conditions, the breakage of the thruster blade and the entanglement of floating objects were selected, and a data-driven method was used to diagnose the faults. In the data-driven method, it is important to select the sensitive fault feature. In this study, vibration, current consumption, rotational speed and input voltage were selected as fault features. An experiment was conducted in an engineering water tank to obtain and analyze data on fault conditions to verify the validity of the selected features. In addition, a new fault diagnosis algorithm combining principal component analysis and Shannon entropy was applied for analyzing the correlations among fault features. This algorithm reduces the dimensionality of data while preserving their structure and characteristics, and diagnoses faults by quantifying entropy values. A fault is detected by comparing the entropy value and a predetermined threshold value, and is diagnosed by analyzing the entropy value and visualized 2D or 3D principal component results. Moreover, the fault diagnosis performance of the unmanned surface vehicle’s thruster was verified by analyzing the results for each fault condition.

Net buoyancy, as the main power source for the motion of an underwater glider, is affected by the pump or bladder that the glider adopts to change its buoyancy force in water. In this study, a new underwater glider that can dive to a depth of 400 m at a cruising speed of 2 knots, which is faster than conventional underwater gliders and is less affected by sea currents, is investigated. The UG resisting 400 m pressure on the buoyancy engine and achieving 2 knots’ speed was designed and constructed. For this UG, its steady-state attitude was studied according to the variance of the buoyancy center and the center of gravity with the buoyancy engine influenced by the displacement of the movable mass block. In motion simulation of the UG, the attitude of the UG under different displacement conditions was simulated in Simulink according to the displacements of the piston and the movable mass block. To validate the simulation performance, a UG was constructed and experiments were conducted. The simulation and experimental results were compared to show the reliability of the simulation results under limited conditions.

A typical structure of an underwater glider (UG) includes a pair of fixed wings, and the hydrodynamic force driving the glider forward as descending or ascending in the water is generated primarily by the fixed wings. In this paper, a simplified glider motion model was established to analyze the dynamics in an easier way, and whose simulation results do not differ from the original one. Also, in the paper, the effects of the wing position and wing shape on the UG to the motion were studied. Since no direct analytic approach cannot be performed, the case study of the effects of six different wing positions and three wing shapes on gliding performances which are gliding speed, gliding angle and gliding path were performed through computer simulation. The simulation results revealed that when the fixed wing is located far from the buoyancy center to the tail end, more traveling range is achieved with less energy. Also, effect of the shape difference of the wings were analyzed. Shape changes did not show much difference on the travelling performance of the UG. In addition to these, the transient mode of the UG was studied. To control this, the PID controller for the position of the mass shifter and piston were applied. By application of the PID controller to the linearized dynamics equations, it was shown that the transient behavior of the UG was quickly and steadily controlled.

Real-time command and data transfer are essential for autonomous underwater vehicle (AUV) motion control in underwater missions. Due to the limitations of underwater acoustic communication, which has a low data rate, this paper introduces a new control structure using underwater wireless optical communication (UWOC) to enable effective real-time command and data transfer. In this control structure, control inputs for the AUV attitude from outside of the water are transferred to the AUV for motion control, while its orientation data and visual images from the AUV camera are sent to the control station outside the water via the UWOC system. For demonstrating the performance of control action and data monitoring, an AUV is built with a constructed UWOC system, two vertical thrusters, and two horizontal thrusters. For attitude control of the AUV, an attitude heading reference system (AHRS) and a depth sensor are installed. Bi-directional communication in the UWOC system is achieved using a return-to-zero (RZ) modulation scheme for faster, longer-range data transfer. A signal processor converts sensor data received from the transmitted data. Finally, the hovering control performance of the AUV equipped with the UWOC system was experimentally evaluated in a water tank, achieving average root mean square errors (RMSEs) of 4.82° in roll, 2.49° in pitch, and 1.99 mm in depth, while simultaneously transmitting real-time motion data at 21.2 FPS with VGA-resolution images (640 × 480 pixels) at a communication rate of 1 Mbps.

The objective of this study was to perform fault diagnosis (FD) specific to various faults that can occur in the thrusters of remotely operated vehicles (ROVs) during hovering control. Underwater thrusters are predominantly utilized as propulsion systems in the majority of ROVs and are essential components for implementing motions such as trajectory tracking and hovering. Faults in the underwater thrusters can limit the operational capabilities of ROVs, leading to permanent damage. Therefore, this study focused on the FD for faults frequently caused by external factors such as entanglement with floating debris and propeller breakage. For diagnosing faults, a data-based technique that identifies patterns according to data characteristics was utilized. In imitation of the fault situations, data for normal, breakage and entangled conditions were acquired, and Density-Based Spatial Clustering of Applications with Noise (DBSCAN) was employed to differentiate between these fault conditions. The proposed methodology was validated by configuring an ROV and conducting experiments in an engineering water tank to verify the performance of the FD.