Tae-Kyeong Yeu

Seabed crushing, a critical underwater operation for mineral resource extraction and infrastructure construction, necessitates acoustic monitoring due to the limited visibility caused by debris generated during the process. This study proposes an acoustic classification method to enable real-time monitoring of underwater robotic operations. Given the acoustic characteristics of crushing operations, which predominantly manifest in the low-frequency band, acoustic features were extracted and processed using a deep learning model to classify the operational states into four categories: idling, cutting, hard cutting, and base. The model was developed using a recurrent neural network (RNN), which is particularly suited for real-time time-series data processing. The classification performance of long short-term memory (LSTM) networks and standard RNN models was systematically evaluated. Training on a dataset collected from crushing operations conducted on land, the LSTM model achieved an accuracy of 89%, outperforming the RNN, which achieved 84%. Furthermore, real-time operational state prediction was performed at a speed of 10 Hz, demonstrating high accuracy. These findings indicate that the proposed method effectively enables real-time classification of seabed crushing operations, thereby enhancing the safety and efficiency of remote underwater operations.

In this study, a method to represent reactive forces at a stick-type controller has been proposed using a haptic master device to effectively communicate work status to users during subsea fracture operations through a teleoperated robot. Estimating reactive forces acting on the tool underwater presents significant challenges. To solve this, we propose a method that combines differential pressure measurement with a deep neural network (DNN) to estimate the reactive forces at the hydraulic manipulator’s tool with good accuracy and a high sampling rate. Specifically, the reactive force was predicted from high-sampling-rate differential pressure data, and the DNN was used to update the reactive force estimation with high accuracy. These tasks were performed recursively within a Kalman filter framework. Finally, a plaster fracture experiment was conducted in a terrestrial environment to verify the proposed method. The estimated reactive forces were compared with those measured by a force-torque sensor using data retrieved from the inertial sensors, joint encoders, and other relevant sensors. The differential pressure-DNN-based approach demonstrated high accuracy in estimating reactive forces in key directions while maintaining fast sampling speed.

Fouling organisms reduce a ship’s fuel efficiency and disturb the ecosystem. Therefore, the International Maritime Organization (IMO) and many nations have enacted laws that mandate periodic hull cleaning for removing fouling organisms. However, cleaning niche areas of the ship hull is extremely difficult. Due to their complex shape, applying antifouling paint and cleaning with hull cleaning robots is difficult, but about 80% of fouling organisms are concentrated in the niche areas. To resolve this issue, this research proposes the use of an autonomous cleaning robot with a hydraulic polyarticular robot arm to clean niche areas of the ship hull. This robot can approach niche areas of the ship hull with complex shapes using its polyarticular arm. It was designed to be able to scan the cleaning area, establish a cleaning plan, and clean accordingly. This robot autonomously cleaned a propeller blade, which is a typical niche area of the ship hull, to verify the applicability of this system. The experiment results show that approximately 80% of the biofouling was removed from the hull crevices and 81% of the cleaned biofouling was recovered.

Monitoring offshore infrastructure is a challenging task owing to the harsh ocean environment. To reduce human involvement in this task, this study proposes an autonomous surface vehicle (ASV)-based structural monitoring system for inspecting power cable lines under the ocean surface. The proposed ASV was equipped with multimodal sonar sensors, including a multibeam echosounder (MBES) and side-scan sonar (SSS) for mapping the seafloor, combined with a precisely estimated vehicle pose from navigation sensors. In particular, a globally consistent map was developed using the orthometric height as a vertical datum estimated based on the geoid height received from the GPS. Accordingly, the MBES and SSS generate a map of the target objects in the form of point clouds and sonar images, respectively. Dedicated outlier removal methods for MBES sensing were proposed to preserve the sparse inlier point cloud, and we applied the projection of the SSS image pixels to reflect the geometry of the seafloor. A field test was conducted in an ocean environment using real offshore cable lines to verify the efficiency of the proposed monitoring system.

Ferromanganese nodules have been recognized as a potential future metal source for over 50 years. Many research and development efforts have been conducted by many organizations. Most of the efforts have been concentrated into the mining technologies especially for hydraulic lifting through riser pipes with bulk-scale nodule collector. However, no commercial mining venture exists. Uncertainty in the economy of nodule mining is considered to be the reason for this. In order to improve the economy, a mining subsystem based on mechanical lifting and small-scale collectors is proposed and the preliminary economic feasibility is examined in this study. The benefit was at a favorable level compared with that using hydraulic lifting with bulk-scale collector. From the viewpoint of environmental impact assessment, environmental considerations of deep-sea sediment plume are explained.

서 론 오늘날 상당수의 수중작업(수중구난, 해양과학탐사, 해저유전 개발 등)에 무인수중잠수정(Unmanned underwater vehicle, UUV) 이 지속적으로 사용되어오고 있으며, 이들 작업은 수중에서의 조작(Manipulation) 기능까지 요구한다. 수중작업에서 가장 일반 적으로 사용되고 있는 ROV(Remotely operated vehicle)는 수상의 선박에 탑승한 2명 이상의 숙련된 운용자에 의해 조종된다. 이 중 한명은 ROV 몸체를, 나머지 한명은 로봇팔을 각각 전담한다. ROV는 선상의 갑판 위에 진⋅회수장치 및 전력공급장치, DPS (Dynamic positioning system) 등을 갖춘