• Proceedings of the Korean Society of Precision Engineering Conference
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• 1995.04b
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• pp.504-509
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• 1995

In this paper, we developed a position correction system of industrial robot for door-chassis assembly task. With the aid of a dedicated vision system, industrial robot accomplished visually acceptable door-chassis's assembly task. The alogorithm of the position detection of notch and 2 dimesional position correction algorithm are noteworthy. The obtained algorithms were satisfatorily implemented for a real door-chassis model.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.1071-1076
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• 2003

In this work, we propose a new framework of robot control for a variety of applications to our unstructured everyday environments. Programming robots can be a very time-consuming process and seems almost impossible for ordinary end users. To cope with this, this work is to provide a software framework for building robot application programs automatically, where we have robots learn how to accomplish a commanded task from the object. An integrated sensing and computing tag is embedded into every single object in the environment. In the robot controller, only the basic software libraries for low-level robot motion control are provided from the robot manufacturer. The main contributions of this work is to develop a server platform that we call Omniscient Server that generates the application programs and send them to the robot controller through the network. The object-related information from the object server merges into robot control software to generate a detailed application program based on the task commands from the human. We have built a test bed and demonstrated that a robot can perform a common household task within the proposed framework.

• Journal of the Korean Society for Precision Engineering
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• v.29 no.9
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• pp.986-994
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• 2012

This paper is concerned with the application of the vision control algorithm with weighting matrix in robot point placement task. The proposed vision control algorithm involves four models, which are the robot kinematic model, vision system model, the parameter estimation scheme and robot joint angle estimation scheme. This proposed algorithm is to make the robot move actively, even if relative position between camera and robot, and camera's focal length are unknown. The parameter estimation scheme and joint angle estimation scheme in this proposed algorithm have form of nonlinear equation. In particular, the joint angle estimation model includes several restrictive conditions. For this study, the weighting matrix which gave various weighting near the target was applied to the parameter estimation scheme. Then, this study is to investigate how this change of the weighting matrix will affect the presented vision control algorithm. Finally, the effect of the weighting matrix of robot vision control algorithm is demonstrated experimentally by performing the robot point placement.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.139-142
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• 1996

This paper presents a method of controlling contact force for deburring tasks. The cope with the nonlinearities and time-varying properties of the robot and the environment, a neural network control theory is applied to design the contact force control system. We show that the contact force between the hand and the contacting surface can be controlled by adjusting the command velocity of a robot hand, which is accomplished by the modeling of a robot and the environment as Mass-Spring-Damper system. Simulation results are shown.

• International Journal of Control, Automation, and Systems
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• v.1 no.3
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• pp.395-400
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• 2003

A quantitative performance evaluation of a robot teaching method using a force/moment direction sensor is presented. The performance of the teaching method using the force/moment direction sensor is compared with the conventional teaching pendant method. Two types of teaching tasks were designed and the teaching times required to complete the teaching tasks were measured and compared. Task A requires a teaching motion that involves four degrees of freedom motion. Task B requires a teaching motion that involves six degrees of freedom motion. It was found that, by using the force/moment direction sensor method, the teaching times were reduced by 25% for Task A and 45% for Task B compared to the teaching pendant method.

• Journal of the Institute of Convergence Signal Processing
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• v.16 no.4
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• pp.168-174
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• 2015

The ultimate purpose of a walking robot is to move to a designated spot and to perform a necessary manipulation. To perform various manipulations for a walking robot, it should have some kind of an extra manipulator. However, if the manipulation task for the robot is simple enough, the robot can perform the task by using its legs. Among various kinds of walking robots, a hexapod walking robot has relatively many legs, so it has the advantage of stability and walking speed. So, a hexapod walking robot can perform simple manipulation task by using its one or two legs while maintaining stability by using the rest of legs. In this paper, we deal with a simple manipulation task of holding a ball. We formulate the task as a redundancy resolution problem and propose a method for obtaining an optimal solution.

• Journal of Computational Design and Engineering
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• v.1 no.4
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• pp.243-255
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• 2014

The present study describes the development of control hardware and software for a mobile welding robot. This robot is able to move and perform welding tasks in a double hull structure. The control hardware consists of a main controller and a welding machine controller. Control software consists of four layers. Each layer consists of modules. Suitable combinations of modules enable the control software to perform the required tasks. Control software is developed using C programming under QNX operating system. For the modularizing architecture of control software, we designed control software with four layers: Task Manager, Task Planner, Actions for Task, and Task Executer. The embedded controller and control software was applied to the mobile welding robot for successful execution of the required tasks. For evaluate this imbedded controller and control software, the field tests are conducted, it is confirmed that the developed imbedded controller of mobile welding robot for shipyard is well designed and implemented.

• The Journal of Korea Robotics Society
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• v.14 no.2
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• pp.81-86
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• 2019

Robots are used in various industrial sites, but traditional methods of operating a robot are limited at some kind of tasks. In order for a robot to accomplish a task, it is needed to find and solve accurate formula between a robot and environment and that is complicated work. Accordingly, reinforcement learning of robots is actively studied to overcome this difficulties. This study describes the process and results of learning and solving which applied reinforcement learning. The mission that the robot is going to learn is bottle flipping. Bottle flipping is an activity that involves throwing a plastic bottle in an attempt to land it upright on its bottom. Complexity of movement of liquid in the bottle when it thrown in the air, makes this task difficult to solve in traditional ways. Reinforcement learning process makes it easier. After 3-DOF robotic arm being instructed how to throwing the bottle, the robot find the better motion that make successful with the task. Two reward functions are designed and compared the result of learning. Finite difference method is used to obtain policy gradient. This paper focuses on the process of designing an efficient reward function to improve bottle flipping motion.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.896-902
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• 2004

The purpose of the study of multi-robot system is to realize multi-robot system easy for the control of robot system in case robot is adapted in the complicated environment of task structure. The purpose of the study of multi-robot system is to realize multi-robot system easy for the control of robot system in case robot is adapted in the complicated environment of task structure. To make real time control possible by making effective use of recognized information in this dynamic environment, suitable distribution of tasks should be made in consideration of function and role of each performing robots. In this paper, IHMA (Intelligent Hybrid Modular Architecture) of Intelligent combined control architecture which utilizes the merits of deliberative and reactive controllers will be suggested and its efficiency will be evaluated through the adaptation of control architecture to representative multi-robot system.

• The Journal of Korea Robotics Society
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• v.17 no.3
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• pp.264-272
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• 2022

Human-robot cooperative tasks are increasingly required in our daily life with the development of robotics and artificial intelligence technology. Interactive reinforcement learning strategies suggest that robots learn task by receiving feedback from an experienced human trainer during a training process. However, most of the previous studies on Interactive reinforcement learning have required an extra feedback input device such as a mouse or keyboard in addition to robot itself, and the scenario where a robot can interactively learn a task with human have been also limited to virtual environment. To solve these limitations, this paper studies training strategies of robot that learn table balancing tasks interactively using deep reinforcement learning with human's facial expression feedback. In the proposed system, the robot learns a cooperative table balancing task using Deep Q-Network (DQN), which is a deep reinforcement learning technique, with human facial emotion expression feedback. As a result of the experiment, the proposed system achieved a high optimal policy convergence rate of up to 83.3% in training and successful assumption rate of up to 91.6% in testing, showing improved performance compared to the model without human facial expression feedback.