Among unmanned surface vehicle (USV) components, underwater thrusters are pivotal in their mission execution integrity. Yet, these thrusters directly interact with marine environments, making them perpetually susceptible to malfunctions. To diagnose thruster faults, a non-invasive and cost-effective vibration-based methodology that does not require altering existing systems is employed. However, the vibration data collected within the hull is influenced by propeller-fluid interactions, hull damping, and structural resonant frequencies, resulting in noise and unpredictability. Furthermore, to differentiate faults not only at fixed rotational speeds but also over the entire range of a thruster’s rotational speeds, traditional frequency analysis based on the Fourier transform cannot be utilized. Hence, Continuous Wavelet Transform (CWT), known for attributions encapsulating physical characteristics in both time-frequency domain nuances, was applied to address these complications and transform vibration data into a scalogram. CWT results are diagnosed using a Vision Transformer (ViT) classifier known for its global context awareness in image processing. The effectiveness of this diagnosis approach was verified through experiments using a USV designed for field experiments. Seven cases with different fault types and severity were diagnosed and yielded average accuracy of 0.9855 and 0.9908 at different vibration points, respectively.

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.

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.

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.

The twin hybrid autonomous underwater vehicle (THAUV) is a novel type of unmanned underwater platform that consists of a twin torpedo-shaped hull and is actuated by two buoyancy engines and two thrusters proposed in this paper. The THAUV was designed to have faster speed generated by the two buoyancy engines and two thrusters. The two buoyancy engines on each hull and the airfoil are mainly responsible for the diving and surfacing motion, and the thrusters drive the THAUV along the horizontal plane. The THAUV is capable of carrying more instrumentation and energy than a conventional hybrid autonomous underwater vehicle (HAUV) with a single buoyancy engine such that the THAUV can perform more exploration tasks and operate for a longer period in a one-time operation. Different from other unmanned underwater vehicles (UUVs) with two airfoils or wings, the THAUV has a single airfoil connecting the twin hull such that it does not require connecting bars and additional airfoils. For this reason, the structure of THAUV is more compact and simpler. In this paper, a new compact THAUV is designed and CFD simulation is used to obtain the hydrodynamic parameters of THAUV operation in water. The motion model of the THAUV is also established and the operating parameters of the THAUV are obtained by simulation.

This study introduces Cylindrical Scan Context (CSC), a novel LiDAR descriptor designed to improve robustness and efficiency in GPS-denied or degraded outdoor environments. Unlike the conventional Scan Context (SC), which relies on azimuth–range projection, CSC employs an azimuth–height representation that preserves vertical structural information and incorporates multiple physical channels—range, point density, and reflectance intensity—to capture both geometric and radiometric characteristics of the environment. This multi-channel cylindrical formulation enhances descriptor distinctiveness and robustness against viewpoint, elevation, and trajectory variations. To validate the effectiveness of CSC, real-world experiments were conducted using both self-collected coastal–forest datasets and the public MulRan–KAIST dataset. Mapping was performed using LIO-SAM with LiDAR, IMU, and GPS measurements, after which LiDAR-only localization was evaluated independently. A total of approximately 700 query scenes (1 m ground-truth threshold) were used in the self-collected experiments, and about 1200 scenes (3 m threshold) were evaluated in the MulRan–KAIST experiments. Comparative analyses between SC and CSC were performed using Precision–Recall (PR) curves, Detection Recall (DR) curves, Root Mean Square Error (RMSE), and Top-K retrieval accuracy. The results show that CSC consistently yields lower RMSE—particularly in the vertical and lateral directions—and demonstrates faster recall growth and higher stability in global retrieval. Across datasets, CSC maintains superior DR performance in high-confidence regions and achieves up to 45% reduction in distance RMSE in large-scale campus environments. These findings confirm that the cylindrical multi-channel formulation of CSC significantly improves geometric consistency and localization reliability, offering a practical and robust LiDAR-only localization framework for challenging unstructured outdoor environments.