• International Journal of Precision Engineering and Manufacturing
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• 제5권3호
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• pp.54-61
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• 2004

This paper describes the development of a 6-axis robot's finger force/moment sensor, which measures forces $F_x$(x-direction force), $F_y$and $F_z$, and moments $M_x$ (x-direction moment), $M_y$ and $M_z$ simultaneously, for stable grasping of an unknown object. In order to safely grasp an unknown object using the robot's gripper, the force in the gripping direction and the force in the gravity direction should be measured, and the force control should be performed using the measured forces. Also, the moments $M_x$, $M_y$ and $M_z$ to accurately perceive the position of the object in the grippers should be detected. Thus, the robot's gripper should be composed of 6-axis robot's finger force/moment sensor that can measure forces $F_x$, $F_y$ and $F_z$, and moments $M_x$ $M_y$ and $M_z$ simultaneously. In this paper, the 6-axis robot's finger force/moment sensor for measuring forces $F_x$, $F_y$ and $F_z$, and moments $M_x$ $M_y$ and $M_z$ simultaneously was newly modeled using several parallel-plate beams, designed, and fabricated. The characteristic test of the fabricated sensor was performed, and the result shows that interference errors of the developed sensor are less than 3%. Also, Robot's gripper with the 6-axis robot's finger force/moment sensor for the characteristic test of force control was manufactured, and the characteristic test for grasping an unknown object using the sensors was performed using it. The fabricated gripper could grasp an unknown object stably. Thus, the developed 6-axis robot's finger force/moment sensor can be used for robot's gripper.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2000년도 제15차 학술회의논문집
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• pp.76-76
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• 2000

The calibration of the robot system with a visual sensor consists of robot, hand-to-eye, and sensor calibration. This paper describe a new technique for computing 3D position and orientation of a 3D sensor system relative to the end effect of a robot manipulator in an eye-on-hand robot configuration. When the 3D coordinates of the feature points at each robot movement and the relative robot motion between two robot movements are known, a homogeneous equation of the form AX : XB is derived. To solve for X uniquely, it is necessary to make two robot arm movements and form a system of two equation of the form: A$_1$X : XB$_1$ and A$_2$X = XB$_2$. A closed-form solution to this system of equations is developed and the constraints for solution existence are described in detail. Test results through a series of simulation show that this technique is simple, efficient, and accurate fur hand/eye calibration.

• 제어로봇시스템학회논문지
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• 제9권10호
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• pp.774-781
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• 2003

The important factors that cause position error in X-Y robot are inertial force, frictions and spring distortion in screw or coupling. We have to estimate these factors precisely to correct position errors, Which is very difficult. In this paper, we makes systems to inspect metal stencil which is used to print solder paste on pads of SMD of PCB with low precision X-Y robot and vision system. To correct position error that is caused by low precision X-Y robot, we defines position error vector that is formed with position of objects that exist in reference and camera image. We apply MHT(Modified Hough Transformation) for the aim of determining the dominant position error vector. We modify reference image using extracted dominant position error vector and obtain reference image that is the same with camera image. Effectiveness and performance of this method are verified by simulation and experiment.

• 한국정밀공학회지
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• 제23권5호
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• pp.77-84
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• 2006

This paper describes the design and implementation of a robot's gripper control system. In order to safely grasp an unknown object using the robot's gripper, the gripper should detect the force of gripping direction and the force of gravity direction, and should perform the force control using the detected forces and the robot's gripper control system. In this paper, the robot's gripper control system is designed and manufactured using DSP(Digital Signal Processor), and the gripper is composed of two 6-axis force/moment sensors which measures the Fx force(force of x-direction), Fy force, Fz force, and the Mx moment(moment of x-direction), My moment, Mz moment at the same time. The response characteristic test of the system is performed to determine the proportional gain Kp and the integral gain Ki of PI controller. As a result, it is shown that the developed robot's gripper control system grasps an unknown object safely.

• 한국정보통신학회논문지
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• 제5권1호
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• pp.144-149
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• 2001

산업현장의 열악한 작업중의 하나인 용접작업을 자동화하기 위한 방법으로 본 논문은 비접촉식 센서인 Fiber Sensor를 이용하여 용접선을 1차적으로 추적하고, 추적한 데이터를 텍스트 파일로 저장함과 통시에 2차적으로 추적한 용접선을 X-Y 로봇의 좌표값으로 다시 출력하여 용접을 하도록 하고 있다. Fiber Sensor를 통하여 읽어 들인 데이터는 Delphi Version 3.0을 이용하여 만들어진 프로그램을 통하여 처리하였고, I/O 입출력은 16 채널의 Relay Actuator 출력과 16 채널의 opto-isolated 입력이 가능한 PC의 ISA슬롯에 직접 삽입하여 사용하는 카드를 통해서 이루어졌다. 본 실험에서 용접선 추적은 직선, 기울기를 가진 직선 및 곡선에 관하여 추적을 행하였고, 추적한 데이터를 토대로 인버터 $CO_2$ 용접기를 사용하여 용접을 행하여 보았다.

• 한국정보통신학회:학술대회논문집
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• 한국해양정보통신학회 2000년도 추계종합학술대회
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• pp.558-561
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• 2000

산업 현장의 열악한 작업중의 하나인 용접작업을 자동화하기 위한 방법으로 본 논문은 비접촉식 센서인 Fiber Sensor를 이용하여 용접선을 1차적으로 추적하고, 추적한 데이터를 텍스트 파일로 저장함과 동시에 2차적으로 추적한 용접선을 X-Y 로봇의 좌표값으로 다시 출력하여 용접을 하도록 하고 있다 Fiber Sensor를 통하여 읽어 들인 데이터는 Delphi Version 3.0을 이용하여 만들어진 프로그램을 통하여 처리하였고, I/O 입출력은 16 채널의 Relay Actuator 출력과 16 채널의 opto-isolated 입력이 가능한 PC의 ISA 슬롯에 직접 삽입하여 사용하는 카드를 통해서 이루어졌다. 본 실험에서 용접선 추적은 직선, 기울기를 가진 직선 및 곡선에 관하여 추적을 행하였고, 추적한 데이터를 토대로 인버터 CO2용접기를 사용하여 용접을 행하여 보았다.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제8권3호
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• pp.185-191
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• 2008

In this paper, experimental studies of a neural network (NN) control technique for non-model based position control of the x-y table robot are presented. Decentralized neural networks are used to control each axis of the x-y table robot separately. For an each neural network compensator, an inverse control technique is used. The neural network control technique called the reference compensation technique (RCT) is conceptually different from the existing neural controllers in that the NN controller compensates for uncertainties in the dynamical system by modifying desired trajectories. The back-propagation learning algorithm is developed in a real time DSP board for on-line learning. Practical real time position control experiments are conducted on the x-y table robot. Experimental results of using neural networks show more excellent position tracking than that of when PD controllers are used only.

• Journal of Biosystems Engineering
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• 제30권5호
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• pp.293-298
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• 2005

The purpose of this study was the development of a multi-joint robot manipulator for milking robot system. The multi-joint robot manipulator was controlled by 5 drivers with driver controller through the position information obtained from the image processing system. The robot manipulator to automatically attach each teat cup to the teats of a milking cow was developed and it's motion was accurately measured with error rate. Results were as follows. 1. Maximum errors in position accuracy were 4mm along X-axis, 4.5mm along Y-axis and 0.9mm along Z-axis. Absolute distance errors were maximum 4.8mm, minimum 2.7mm, and average 3.6mm. 2. Errors of repeatability were maximum 3.0mm along X-axis, 3.0mm along Y-axis, and 0.5mm along Z-axis. Distance error values were maximum 3.2mm, minimum 2.2mm, and average 2.5mm. It is envisaged that multi-joint robot manipulator can be applicate to milking robot system being developed in consideration of the experiment results.

• 한국축산시설환경학회지
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• 제6권2호
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• pp.105-112
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• 2000

본 연구는 로봇착유기 개발을 위한 기초연구로서 유두위치 측정장치와 로봇제어 시스템을 구성하고 그 성능을 분석하고자 수행되었으며 그 결과는 다음과 같다. 1. 유두인식은 스테레오 측량법에 기초한 이미지 장치로 처리했으며 유두인식오차는 (x, y, z) = (0.83, 1.95, 0.81)mm이다. RCS로 좌표값을 변환했을 때 시스템 전체의 오차는 x = 0.9mm, y = 2.0mm, z = 0.9mm였다. 2. 로봇 유두컵 착탈 시스템의 로봇 착탈 성공률은 평균 91.5%로 나타났다. 작업시간은 27.8sec였으며, 이미지 처리를 포함한 전체 작업시간은 86.1sec로 나타났다.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2001년도 하계학술대회 논문집 D
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• pp.2365-2368
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• 2001

This paper describes a fuzzy steering controller for an autonomous mobile robot with MR sensor. Using the magnetic field($B_{x}$, $B_{y}$, $B_{z}$) obtained from the MR sensor, we designed fuzzy controller for driving on the road center. Fuzzy rule base was built to magnetic field($B_{x}$, $B_{y}$, $B_{z}$). To develop an autonomous mobile robot simulation program, we have done modeling MR sensor, dynamic model of mobile robot and coordinate transformation. A computer simulation of the robot (including mobile robot dynamics and steering) was used to verify the steering performance of the mobile robot controller using the fuzzy logic. Good results were obtained by computer simulation. So, we confirmed the robustness of the proposed fuzzy controller by computer simulation. Also, we know that proposed control algorithm was applied to real autonomous mobile robot.