• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.1116-1120
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• 2004

In the nuclear power plant, there are several cylindrical vessels such as reactor vessel, pressuriser and so on. The vessels are usually constructed by welding large rolled plates, forged sections or nozzle pipes together. In order to assure the integrity of the vessel, these welds should be periodically inspected using sensors such as ultrasonic transducer or visual cameras. This inspection is usually conducted under water to minimize exposure to the radioactively contaminated vessel walls. The inspections have been performed by using a conventional inspection machine with a big structural sturdy column, however, it is so huge and heavy that maintenance and handling of the machine are extremely difficult. It requires much effort to transport the system to the site and also requires continuous use of the utility's polar crane to move the manipulator into the building and then onto the vessel. Setup beside the vessel requires a large volume of work preparation area and several shifts to complete. In order to resolve these problems, we have developed an underwater mobile robot guided by the laser pointer, and performed a series of experiments both in the mockup and in the real reactor vessel. This paper introduces our robotic inspection system and the laser guidance of the mobile robot as well as the results of the functional test.

• The Journal of the Korea institute of electronic communication sciences
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• v.8 no.1
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• pp.27-40
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• 2013

This study is about development of technology system for an advanced deep-sea unmanned underwater robot of S. Korea analysed by the application of scenario planning. It was developed a 6000m class next-generation deep-sea unmanned underwater vehicle(or robot, UUV) system, soonly ROV 'Hemire' and Depressor 'Henuvy' in 2006 at S. Korea and motion control, adaptive control algolithm, a work-space manipulator control algolithm, especially the underwater inertial-acoustic navigation system robust to initial errors and sensor failures. But there are remained matters on position tracking of the USBL, inertial-acoustic navigation system, attitude sensor, designed sonar sensors. So this study suggest the new idea for settle the matters and then this idea help the development of the underwater inertial-acoustic navigation system robust to initial errors and sensor failures, such as acoustic signal drop-out, by modifying the error covariance of the failed sonar signal when drop-out occurs. As a result, the future policy for deep-sea unmanned underwater robot of S. Korea is to further spur the development of new technology and more improvement of the technology level for deep-sea unmanned underwater robot system with indicator and imaginary wall as external device.

• Journal of the Korean Society for Precision Engineering
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• v.30 no.9
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• pp.961-968
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• 2013

In this paper, we propose an optimal design for starfish capturing manipulator module with four-bar linkage mechanism. A tool link with compliance is attached on the four-bar linkage, and the tool repeats detaching starfish from the ground and putting it into the storage box. Since the tool is not rigid and the manipulator is operating underwater, the trajectory of the tool tip is determined by its dynamics as well as kinematics. We analyzed the trajectory of the manipulator tool tip by quasi-static analysis considering both kinematics and dynamics. In optimization, the lengths of each link and the tool stiffness are considered as control variables. To maximize the capturing ability, capturing stroke of the four-bar manipulator trajectory is maximized. Reaction force and reaction moment, and other kinematic constraints were considered as inequality constraints.

• Journal of the Korean Society for Precision Engineering
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• v.17 no.1
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• pp.138-144
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• 2000

Robot manipulators, which are nonlinear structures and have uncertain system parameters, have complex in dynamics when are operated in unknown environment. To compensate for estimate errors of the uncertain system parameters and to accomplish the desired trajectory tracking, nonlinear robust controllers are appropriate. However, when estimation errors or tracking errors are large, they require large input torques, which may not be satisfied due to torque limits of actuators. As a result, their stability can not be guaranteed. In this paper, a new robust control scheme is presented to solve stability problem and to achieve fast trajectory tracking in the presence of torque limits. By using fuzzy logic, new desired trajectories which can be reduced are generated based on the initial desired trajectory, and torques of the robust controller are regulated to not exceed torque limits. Numerical examples are shown to validate the proposed controller using an uncertain two degree-of-freedom underwater robot manipulator.

• Journal of Advanced Research in Ocean Engineering
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• v.3 no.3
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• pp.136-148
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• 2017

The paper presents dual arm ROV manipulation using deep reinforcement learning. The purpose of this underwater manipulator is to investigate and excavate natural resources in ocean, finding lost aircraft blackboxes and for performing other extremely dangerous tasks without endangering humans. This research work emphasizes on a self-learning approach using Deep Reinforcement Learning (DRL). DRL technique allows ROV to learn the policy of performing manipulation task directly, from raw image data. Our proposed architecture maps the visual inputs (images) to control actions (output) and get reward after each action, which allows an agent to learn manipulation skill through trial and error method. We have trained our network in simulation. The raw images and rewards are directly provided by our simple Lua simulator. Our simulator achieve accuracy by considering underwater dynamic environmental conditions. Major goal of this research is to provide a smart self-learning way to achieve manipulation in highly dynamic underwater environment. The results showed that a dual robotic arm trained for a 3DOF movement successfully achieved target reaching task in a 2D space by considering real environmental factor.