• Title, Summary, Keyword: Joint Self-Sensor

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Reliability Monitoring of Adhesive Joints by Piezoelectricity (압전특성을 이용한 접착 조인트의 안전성 모니터링)

  • Kwon, Jae-Wook;Chin, Woo-Seok;Lee, Dai-Gil
    • Transactions of the Korean Society of Mechanical Engineers A
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    • v.27 no.8
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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.

A Study of Rotating Arc Sensor Using Fuzzy Controller for$CO_2$ Arc Welding ($CO_2$ 아크 용접에서 퍼지 제어기를 이용한 회전 아크센서에 관한 연구)

  • Choi Youngsoo;Park Hyunsung
    • Transactions of the Korean Society of Machine Tool Engineers
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    • v.13 no.5
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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.

Robust Control of AM1 Robot Using PSD Sensor and Back Propagation Algorithm (PSD 센서 및 Back Propagation 알고리즘을 이용한 AM1 로봇의 견질 제어)

  • Jung, Dong-Yean;Han, Sung-Hyun
    • Journal of the Korean Society of Industry Convergence
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    • v.7 no.2
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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.

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GEOMETRY OF SATELLITE IMAGES - CALIBRATION AND MATHEMATICAL MODELS

  • JACOBSEN KARSTEN
    • Proceedings of the KSRS Conference
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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.

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Work chain-based inverse kinematics of robot to imitate human motion with Kinect

  • Zhang, Ming;Chen, Jianxin;Wei, Xin;Zhang, Dezhou
    • ETRI Journal
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    • v.40 no.4
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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.

Robust Control of Industrial Robot Based on Back Propagation Algorithm (Back Propagation 알고리즘을 이용한 산업용 로봇의 견실 제어)

  • 윤주식;이희섭;윤대식;한성현
    • Proceedings of the Korean Society of Machine Tool Engineers Conference
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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.

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Robust control of industrial robot using back propagation algorithm and PSD (역전파 알고리즘 및 PSD를 이용한 로봇의 결실제어)

  • 이재욱
    • Proceedings of the Korean Society of Machine Tool Engineers Conference
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    • pp.171-175
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    • 2000
  • Neural networks are 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 training and processing implementation required for real time control.

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Design of Industrial Robot Control System Using PSD and Back Propagation Algorithm (PSD 및 역전파 알고리즘을 이용한 산업용 로봇의 제어 시스템 설계)

  • 이재욱;이희섭;김휘동;김재실;한성현
    • Proceedings of the Korean Society of Machine Tool Engineers Conference
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    • pp.108-112
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    • 2000
  • 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 training and processing implementation required for real time control.

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Design of AM1 Robot Control System Using PSD and Back Propagation Algorithm (PSD 및 역전파 알고리즘를 이용한 AM1 로봇의 제어 시스템 설계)

  • 이재욱;서운학;이종붕;이희섭;한성현
    • Proceedings of the Korean Society of Machine Tool Engineers Conference
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    • pp.239-243
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    • 2001
  • 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 training and processing implementation required for real time control.

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A neural network based real-time robot tracking controller using position sensitive detectors (신경회로망과 위치 검출장치를 사용한 로보트 추적 제어기의 구현)

  • 박형권;오세영;김성권
    • 제어로봇시스템학회:학술대회논문집
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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.

  • PDF