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.

The purpose of this study is to design a Swarm Control algorithm for the effective mission performance of multiple unmanned surface vehicles (USVs) used for marine research purposes at sea. For this purpose, external force information was utilized for the control of multiple USV swarms using a lead–follow-formation technique. At this time, to efficiently control multiple USVs, the LSTM algorithm was used to learn ocean currents. Then, the predicted ocean currents were used to control USVs, and a study was conducted on behavioral-based control to manage USV formation. In this study, a control system model for several USVs, each equipped with two rear thrusters and a front lateral thruster, was designed. The LSTM algorithm was trained using historical ocean current data to predict the velocity of subsequent ocean currents. These predictions were subsequently utilized as system disturbances to adjust the controller’s thrust. To measure ocean currents at sea as each USV moves, velocity, azimuth, and position data (latitude, longitude) from the GPS units mounted on the USVs were utilized to determine the speed and direction of the hull’s movement. Furthermore, the flow rate was measured using a flow rate sensor on a small USV. The movement and position of the USV were regulated using an Artificial Neural Network-PID (ANN-PID) controller. Subsequently, this study involved a comparative analysis between the results obtained from the designed USV model and those simulated, encompassing the behavioral control rules of the USV swarm and the path traced by the actual USV swarm at sea. The effectiveness of the USV mathematical model and behavior control rules were verified. Through a comparison of the movement paths of the swarm USV with and without the disturbance learning algorithm and the ANN-PID control algorithm applied to the designed simulator, we analyzed the position error and maintenance performance of the swarm formation. Subsequently, we compared the application results.

The suction pile can be installed in a slope when it is penetrated in the seabed owing to the inhomogeneity of the ground. Therefore, a method for the verticality correction of suction pile is required. In this study, to correct the verticality of the suction pile while using the suction penetration process, the concept to control the length of the lifting cable was devised and a model test was performed. The results showed that in the case of individual cable control technique according to the slope condition, the verticality was corrected by inducing rotation due to the difference in tension in the individual cable. It was 0.1° when the suction pile finished penetration. Furthermore, the penetration velocity did not reduce while correcting the verticality during the penetration process.

Securing the verticality of foundations is a crucial factor for ensuring safety in offshore construction. The repeated intrusion-pulling method is generally used to ensure verticality in suction bucket foundation construction processes. However, it relies heavily on the experience and skills of field workers and is relatively time-consuming. To address this problem, we propose an automatic verticality securing system for large circular steel pipes based on a verticality monitoring system. This system adjusts cables at locations where verticality correction is required without changing the existing suction pile–penetration–construction process. It includes a monitoring component that provides real-time data on pipe alignment and an automatic lifting cable control system that maintains perpendicularity using data acquired from the monitoring system. The monitoring system comprises a sensor, an embedded controller, and a display program that displays the vertical information of circular steel pipes. The automatic lifting cable control system includes a controller with an algorithm for adjusting the length of the actuator. We showed that the system operates satisfactorily and secures the verticality of less than 0.2° in the suction bucket-based model experiment. Furthermore, the testbed experimental results show that our monitoring system can efficiently measure verticality information in real time.

The foundations of offshore wind power can be classified as floating, tripod, jacket, monopile, or gravity-based, depending on the support type. In the case of tripod- and jacket-type supports, the structures require precise construction. There are two main methods for installing substructures: post- and pre-piling. The post-piling method involves moving the completed substructure to the site and fixing it to the seabed by inserting a pile into the leg pile and driving it, allowing it to be constructed without special off-shore equipment; however, the construction period is long. Contrarily, the precision of foundation installation can be improved by installing a pre-piling template, which is special equipment that serves as a basic structure, on the seabed in advance, and subsequently inserting substructures. This study presents a new type of underwater pre-piling template and method for achieving optimal construction environment conditions. Construction precision was analyzed based on the wave condition, current speed, winch speed, wave direction, and current direction while the under-water template was anchored to the seabed. It was found that the wave conditions, winch speed, and vessel type had a significant influence. The results obtained considering the Douglas sea scale show that precise construction could only be achieved within Grade 2 for general barge ships, while jack-up barge ships could be used even at Grade 3 or higher. The higher the winch speed, the more stable construction becomes possible, and jack-up barges show greater constructability than general barges.

This study aimed to learn disturbance information to effectively control multiple small unmanned surface vehicles (USVs) for marine research purposes at sea. The learned disturbance information was used to investigate the control of multiple USV swarms based on the leading–following formation technique. Numerous studies have already been conducted on swarm control using drones in the air; however, there has been a paucity of studies on swarm control in the sea. Contrary to swarm control in aviation, the fluid density of the sea is high, and the environment is constantly changing; thus, a robot system would be significantly affected by disturbances. This ultimately influences the maintenance of swarm formations and the accomplishment of missions [1,2]. Therefore, it is difficult to control numerous moving objects while maintaining a formation at sea by using the existing aerial drone swarm control technology. To solve this problem, we learned a long short-term memory (LSTM) model with time-series marine environmental data to predict maritime disturbances. Thereafter, the model was applied to a mission-based position control algorithm of each USV constituting the swarm to design a marine unmanned floating vessel swarm control system that can be applied to various marine environments. Specifically, to effectively control numerous small USVs for marine research purposes, ocean currents are learned using the LSTM algorithm, and the predicted ocean currents are used to generate a swarm USV control system for USV formations. In this study, a control system model of several small USVs equipped with two rear thrusters and a front lateral thruster was designed. The LSTM algorithm was learned using the previous ocean current data, and the velocity of the following ocean currents was predicted. The predictions were then used as a system disturbance to determine the thrust of the controller. To measure ocean currents in the sea when each USV moves, the velocity, azimuth, and position (latitude, longitude) data from the GPS mounted on the USV were used to measure the velocity and direction of the hull's movement. Further, the flow rate was measured using a flow rate sensor on a small USV. The USV movement and position were controlled using an artificial neural network-PID (ANN-PID) controller. This study entailed a comparative analysis of the designed USV model results and those generated by the simulator, including the behavior control rule of the USV swarm and the path of the actual USV swarm at sea. It was verified that the effectiveness of the USV mathematical model and behavior control rules. By comparing the movement path of the swarm USV with and without the disturbance learning algorithm and ANN-PID control algorithm applied to the designed simulator, the position error and maintenance performance of the swarm formation were analyzed, and the application results were compared.