• 제어로봇시스템학회:학술대회논문집
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• 1996.10a
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• pp.251-254
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• 1996

We propose a coordinated motion control algorithm of dual manipulators handling a flexible object. The controller is designed so that it can specify the apparent impedance of the object as well as can control its deformation. The experimental results will illustrate validity of the proposed algorithm.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2011.06a
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• pp.289-290
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• 2011

• The Journal of Korea Robotics Society
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• v.10 no.1
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• pp.42-51
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• 2015

Peg-in-hole assembly is the most representative task for a robot to perform under contact conditions. Various strategies for accomplishing the peg-in-hole task with a robot exist, but the existing strategies are not sufficiently practical to be used for various assembly tasks in a human environment because they require additional sensors or exclusive tools. In this paper, the peg-in-hole assembly experiment is performed with anthropomorphic hand arm robot without extra sensors or devices using "intuitive peg-in-hole strategy". From this work, the probability of applying the peg-in-hole strategy to a common assembly task is verified.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.1093-1098
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• 2003

In this paper, a practical methodology of hand-manipulator motion coordination for indoor service robot is introduced. This paper describes the procedures of opening door performed by service robot as a noticeable example of motion coordination. This paper presents well-structured framework for hand-manipulator motion coordination, which includes intelligent sensor data interpretation, object shape estimation, optimal grasping, on-line motion planning and behavior-based task execution. This proposed approach is focused on how to integrate the respective functions in harmony and enable the robot to complete its operation under the limitation of usable resources. As a practical example of implementation, the successful experimental results in opening door whose geometric parameters are unknown beforehand are provided.

• Proceedings of the KIEE Conference
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• 2007.04a
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• pp.331-333
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• 2007

Robot manipulator is one of the important features in service robot area. Until now, there has been a lot of research on robot" manipulator that can imitate the functions of a human being by recognizing and grasping objects. In this paper, we present a robot arm based on the object recognition vision system. We have implemented closed-loop control that use the feedback from visual information, and used a sonar sensor to improve the accuracy. We have placed the web-camera on the top of the hand to recognize objects. We also present some vision-based manipulation issues and our system features.

• Electronics and Telecommunications Trends
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• v.36 no.2
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• pp.22-31
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• 2021

This report introduces a definition for robot task intelligence and explains the basis of the challenges associated with implementing robot task intelligence in real-world problems. Two fundamental elements of this intelligence, robot manipulation and navigation, are introduced herein. We describe the existing trends and industrial applications of the robot task intelligence in logistics and agricultural automation. Furthermore, as an underpinning technology for this intelligence, we review the existing three-dimensional position estimation techniques and summarize the difficulties associated with applying pure SLAM technology to real-world applications. Finally, we discuss the prospects of the robot task intelligence research and its potential for solving real-world problems.

• Journal of the Korean Institute of Telematics and Electronics
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• v.24 no.2
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• pp.223-229
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• 1987

This robot programming system is designed for off-line programming of numerical controlled robot manipulator. This system consists of manipulation interpreter, world model manager, graphic simulator and arm controller for simple robot programming language. The system has graphic simulation system as a debugging tool for task programming and it simulates the robot motion graphically on a CRT terminal, which makes the assessment of the possible robot motion.

• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.3
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• pp.507-514
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• 2022

Mobile manipulators are getting lime light in the field of home automation due to their mobility and manipulation capabilities. In this paper, we developed a small size manipulator system that can be mounted on a mobile robot as a preliminary study to develop a mobile manipulator. The developed manipulator has four degree-of-freedom. At the end-effector of manipulator, there are a camera and a gripper to recognize and manipulate the object. One of four degree-of-freedom is linear motion in vertical direction for better interaction with human hands which are located higher than the mobile manipulator. The developed manipulator was designed to dispose the four actuators close to the base of the manipulator to reduce rotational inertia of the manipulator, which improves stability of manipulation and reduces the risk of rollover. The developed manipulator repeatedly performed a pick and place task and successfully manipulate the object within the workspace of 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.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2007.11a
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• pp.1203-1210
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• 2007

Robots have begun to perform various tasks on replacing the human in the daily life such as cleaning, entertainments etc. In order to accomplish the effective performance of intricate and precise tasks, robot hand must have special capabilities, such as decision making in given condition, autonomy in unknown situation and stable manipulation of object. In this study, we addresses the development of a 3-fingered humanoid robot hand system. We execute static analysis, vibration analysis and flexible dynamics to reserve stability at the design. Grasp motion of the finger uses a linear actuator and gears. Motion can be distinguished into four parts depending on the grasping thin paper, sphere, and column. In each motion, we compare the displacement of the case to be rigid with the case to be flexible. As a result, manufactured and feasibility of the robot hand is validated through preliminary experiments.