• 한국공작기계학회논문집
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• 제13권5호
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• pp.110-117
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• 2004

In automatic welding process using a robot, seam tracking is one of the important parts. Sensor for seam tracking is divided broadly into two categories as non contact sensor and contact sensor. The arc sensor is one of the non contact sensors, and it can be applied in weaving arc and rotating arc welding process. In such the arc sensors, rotating arc sensor can be applied to high speed welding over tens of Hz. The decrease of self regulation by high rotating speed causes to improve accuracy and response of sensor. In this study, fuzzy controller was used to track the seam for the $CO_2$ arc welding which had unstable arc. It could be shown that the rotating arc sensor was better than the weaving arc sensor.

• 대한기계학회논문집A
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• 제27권8호
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• pp.1388-1397
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• 2003

Since the reliability of adhesively bonded joints for composite structures is dependent on many parameters such as the shape and dimensions of joints, type of applied load, and environment, so an accurate estimation of the fatigue life of adhesively bonded joints is seldom possible, which necessitates an in-situ reliability monitoring of the joints during the operation of structures. In this study, a self-sensor method for adhesively bonded joints was devised, in which the adhesive used works as a piezoelectric material to send changing signals depending on the integrity of the joint. From the investigation, it was found that the electric charge increased gradually as cracks initiated and propagated in the adhesive layer, and had its maximum value when the adhesively bonded joint failed. So it is feasible to monitor the integrity of the joint during its lifetime. Finally, a relationship between the piezoelectric property of the adhesive and crack propagation was obtained from the experimental results.

• 한국산업융합학회 논문집
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• 제7권2호
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• pp.167-172
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• 2004

Neural networks are used in the framework of sensor based tracking control of robot manipulators. They learn by practice movements the relationship between PSD(an analog Position Sensitive Detector) sensor readings for target positions and the joint commands to reach them. Using this configuration, the system can track or follow a moving or stationary object in real time. Furthermore, an efficient neural network architecture has been developed for real time learning. This network uses multiple sets of simple back propagation networks one of which is selected according to which division (Corresponding to a cluster of the self-organizing feature map) in data space the current input data belongs to. This lends itself to a very training and processing implementation required for real time control.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2005년도 Proceedings of ISRS 2005
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• pp.182-185
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• 2005

Satellite cameras are calibrated before launch in detail and in general, but it cannot be guaranteed that the geometry is not changing during launch and caused by thermal influence of the sun in the orbit. Modem satellite imaging systems are based on CCD-line sensors. Because of the required high sampling rate the length of used CCD-lines is limited. For reaching a sufficient swath width, some CCD-lines are combined to a longer virtual CCD-line. The images generated by the individual CCD-lines do overlap slightly and so they can be shifted in x- and y-direction in relation to a chosen reference image just based on tie points. For the alignment and difference in scale, control points are required. The resulting virtual image has only negligible errors in areas with very large difference in height caused by the difference in the location of the projection centers. Color images can be related to the joint panchromatic scenes just based on tie points. Pan-sharpened images may show only small color shifts in very mountainous areas and for moving objects. The direct sensor orientation has to be calibrated based on control points. Discrepancies in horizontal shift can only be separated from attitude discrepancies with a good three-dimensional control point distribution. For such a calibration a program based on geometric reconstruction of the sensor orientation is required. The approximations by 3D-affine transformation or direct linear transformation (DL n cannot be used. These methods do have also disadvantages for standard sensor orientation. The image orientation by geometric reconstruction can be improved by self calibration with additional parameters for the analysis and compensation of remaining systematic effects for example caused by a not linear CCD-line. The determined sensor geometry can be used for the generation? of rational polynomial coefficients, describing the sensor geometry by relations of polynomials of the ground coordinates X, Y and Z.

• ETRI Journal
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• 제40권4호
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• pp.511-521
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• 2018

The ability to realize human-motion imitation using robots is closely related to developments in the field of artificial intelligence. However, it is not easy to imitate human motions entirely owing to the physical differences between the human body and robots. In this paper, we propose a work chain-based inverse kinematics to enable a robot to imitate the human motion of upper limbs in real time. Two work chains are built on each arm to ensure that there is motion similarity, such as the end effector trajectory and the joint-angle configuration. In addition, a two-phase filter is used to remove the interference and noise, together with a self-collision avoidance scheme to maintain the stability of the robot during the imitation. Experimental results verify the effectiveness of our solution on the humanoid robot Nao-H25 in terms of accuracy and real-time performance.

• 한국공작기계학회:학술대회논문집
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• 한국공작기계학회 2004년도 춘계학술대회 논문집
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• pp.253-257
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• 2004

Neural networks are works are used in the framework of sensor based tracking control of robot manipulators. They learn by practice movements the relationship between PSD(an analog Position Sensitive Detector) sensor readings for target positions and the joint commands to reach them. Using this configuration, the system can track or follow a moving or stationary object in real time. Furthermore, an efficient neural network architecture has been developed for real time learning. This network uses multiple sets of simple back propagation networks one of which is selected according to which division(corresponding to a cluster of the self-organizing feature map) in data space the current input data belongs to. This lends itself to a very training and processing implementation required for real time control.

• 한국전자통신학회논문지
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• 제19권1호
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• pp.85-92
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• 2024

본 논문에서는 로봇팔의 선단을 잡고 원하는 위치로 이동시켜서 작업을 직접 교시하는 직감적인 교시 및 제어를 시스템을 개발하였다. 개발된 시스템은 로봇팔 선단부의 위치 방향 및 자세 방향 힘을 측정하는 6축힘 센서, 선단부에서 측정된 힘에 의한 로봇팔 관절 속도제어 명령어 생성 알고리즘, 자체 제작한 6축 로봇팔 및 제어 시스템으로 구성된다. 선단부 핸들러에 부착된 힘센서를 통해 로봇팔 조작자가 핸들러를 조종하는 위치 자세의 6차원의 힘/토크를 감지하고 이를 선단부 조종속도 명령으로 변환하여 6축 로봇팔을 제어한다. 연구 방법의 검증은 자체 제작된 6축 로봇으로 실행하였으며, 조종자의 핸들러 조정을 통한 작업교시에 의한 실험을 통해 제안한 힘 센서기반 로봇 선단 제어 방법이 성공적으로 동작함을 확인하였다.

• 대한전기학회논문지
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• 제39권12호
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• pp.1306-1316
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• 1990

A controller using a multilayer neural network is proposed to the dynamic control of a PUMA 560 robot arm. This controller is developed based on an error back-propagation (BP) neural network. Since the neural network can model an arbitrary nonlinear mapping, it is used as a commanded feedforward torque generator. A Proportional Derivative (PD) feedback controller is used in parallel with the feedforward neural network to train the system. The neural network was trained by the current state of the manipulator as well as the PD feedback error torque. No a priori knowledge on system dynamics is needed and this information is rather implicitly stored in the interconnection weights of the neural network. In another experiment, the neural network was trained with the current, past and future positions only without any use of velocity sensors. Form this thim window of position values, BP network implicitly filters out the velocity and acceleration components for each joint. Computer simulation demonstrates such powerful characteristics of the neurocontroller as adaptation to changing environments, robustness to sensor noise, and continuous performance improvement with self-learning.

• 한국공작기계학회:학술대회논문집
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• 한국공작기계학회 2002년도 춘계학술대회 논문집
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• pp.393-398
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• 2002

Neural networks are used in the framework of sensorbased tracking control of robot manipulators. They learn by practice movements the relationship between PSD (an analog Position Sensitive Detector) sensor readings for target positions and the joint commands to reach them. Using this configuration, the system can track or follow a moving or stationary object in real time. forthermore, an efficient neural network architecture has been developed for real time learning. This network uses multiple sets of simple backpropagation networks one of which is selected according to which division (corresponding to a cluster of the self-organizing feature map) in data space the current input data belongs to. This lends itself to a very training and processing implementation required for real time control.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1993년도 한국자동제어학술회의논문집(국내학술편); Seoul National University, Seoul; 20-22 Oct. 1993
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• pp.660-665
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• 1993

Neural networks are used in the framework of sensorbased tracking control of robot manipulators. They learn by practice movements the relationship between PSD ( an analog Position Sensitive Detector) sensor readings for target positions and the joint commands to reach them. Using this configuration, the system can track or follow a moving or stationary object in real time. Furthermore, an efficient neural network architecture has been developed for real time learning. This network uses multiple sets of simple backpropagation networks one of which is selected according to which division (corresponding to a cluster of the self-organizing feature map) in data space the current input data belongs to. This lends itself to a very fast training and processing implementation required for real time control.