In this paper, in order to overcome certain limitations of previously commercialized platforms, a new integrated unmanned surface vehicle (USV) and unmanned underwater vehicle (UUV) platform connected via underwater cable capable of acquiring real-time underwater data and long-time operation are studied. A catamaran-type USV was designed to overcome the limitations of an ocean environment and to play the role as the hub of power supply and communication for the integrated platform. Meanwhile, the UUV was designed as torpedo-shaped to minimize hydrodynamic resistance and its hardware design was focused on processing and sending the underwater camera and sonar data. The underwater cable driven by a winch system was installed to supply power from the USV to the UUV and to transmit acquired data form underwater sonar sensor or camera. Different from other previously studied cooperation system of USVs and autonomous underwater vehicles (AUVs), the merit of the proposed system is real-time motion coordination control between the USV and UUV while transmitting large amount of data using the tether cable. The main focus of the study is coordination of the UUV with respect to the global positioning system (GPS) attached at USV and verification of its performance throughout field tests. Waypoint tracking control algorithm was designed and implemented on USV and relative heading, distance control for USV–UUV coordination was implemented to UUV. To ensure the integrity of the coordination control of the integrated platform, a study on accurate measurement system of the relative position between the USV and the UUV by using the GPS and the ultrashort baseline (USBL) device was performed. Individual tests were conducted to verify the performance of USBL and AHRS, which provide the position and heading data of UUV among the sensors mounted on the actual platform, and the effectiveness of the obtained sensor data is presented. Using the accurate measurement system, a number of field tests were conducted to verify the performance of the integrated platform.

We modeled and implemented a joint airborne system integrating ground penetrating radar (GPR) and magnetometer (MAG) models specifically for landmine detection applications. We conducted both simulations and experimental analyses of the joint airborne GPR and MAG models, with a focus on detecting the metallic components of different types of landmines, including antitank (AT) M15 metallic, antipersonnel (AP) M16 metallic, and AT M19 plastic (minimum-metal) landmines. The GPR model employed the finite-difference time-domain (FDTD) method and was evaluated using a singular value decomposition (SVD) and Kirchhoff migration (KM) with matched filtering (MF). These advanced techniques enabled the automatic identification and precise focusing of the reflected hyperbolic signals emitted by the landmines while considering cross-range resolution. Additionally, the MAGs were utilized based on the magnetic dipole model with a de-trend and a spatial median filtering method to estimate the magnetic anomaly of the landmines while considering various data spatial intervals. The joint airborne GPR and MAG system was implemented by combining and integrating the GPR and MAG models for experimental validation. Through this comprehensive approach, which included experiments, simulations, and data processing, the design parameters of the final system were obtained. These design parameters can be used in the development and application of landmine detection systems based on airborne GPR and MAG technology.

In this article, a design and analysis on 200-m-class hybrid underwater glider actuated by a buoyancy engine and a thruster was studied. The hull shape of the hybrid underwater glider was designed to reduce underwater resistance using Myring hull profile equations. A computational fluid dynamics analysis was conducted, and a resistance coefficient similar to that of existing underwater vehicle was calculated such that simulation reliability was verified. The relationship between the control value of the ballast discharged by the buoyancy engine and the velocity of a glider according to the path angle was analyzed. Through the analysis, the relationship between the optimal glide angle and the designed control value of the ballast water was drawn, and using this relationship, the optimal glider velocity was estimated. Also, the velocity of the hybrid underwater glider using the thruster only was measured through an experiment. Using both the thruster and ballast water control, it was shown that the maximum velocity of the developed hybrid underwater glider is over 2.4 knots.

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.

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.

To operate unmanned systems in the ocean, many elements are required. They could be factors related to the equipment performance or operating methods. However, the aforementioned factors are suitable for situations not assuming unexpected events; in the actual ocean, unmanned equipment has the potential to experience problems in operation due to several factors such as environmental or external forces. Having problems in the propulsion system of unmanned marine equipment in unexpected situations affects the execution of duty of the unmanned equipment and connotes the risk of equipment damage and loss. Also, a lot of data generated in the real world is distributed disproportionately for each class, which means that the data of the minor class consists of significantly less data than the data of the majority class, and the normal data, in contrast, anomaly data is a very small amount of data, and there is very little data for learning. Therefore, in this study, anomaly detection based on unsupervised learning was conducted about these problems. In this study, we evaluated anomaly detection based on unsupervised learning to resolve these problems. Based on the results, the state of unmanned systems being operated in the ocean are identified and the anomaly state is examined. In this study, the auto-encoder (AE) method that learns the features of the normal area of the judgment data, that is, a part of machine learning, and the LSTM method for predicting time series states are utilized. This machine learning technology uses time-series learning data on the state of the unmanned system, creates standards to determine normal and anomaly states, and in turn, learns the normal state and determines the anomaly state. Furthermore, the simulation of the proposed method was carried out to verify the algorithm used in this study, and its validity was verified by comparing its data with the measured data. By applying the deduced anomaly detection information to the unmanned surface vehicle (USV) simulator, a fault tolerance control system was designed to enable the execution of duty even in the situation of an anomaly propeller of the USV, and the effectiveness of this study was verified.