• Transactions of the Korean Society of Mechanical Engineers A
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• v.37 no.4
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• pp.447-459
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• 2013

Many problems need to be solved before vision systems can actually be applied in industry, such as the precision of the kinematics model of the robot control algorithm based on visual information, active compensation of the camera's focal length and orientation during the movement of the robot, and understanding the mapping of the physical 3-D space into 2-D camera coordinates. An algorithm is proposed to enable robot to move actively even if the relative positions between the camera and the robot is unknown. To solve the correction problem, this study proposes vision system model with six camera parameters. To develop the robot vision control algorithm, the N-R and EKF methods are applied to the vision system model. Finally, the position accuracy and processing time of the two algorithms developed based based on the EKF and the N-R methods are compared experimentally by making the robot perform slender bar placement task.

• TTA Journal
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• s.83
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• pp.122-126
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• 2002

한국정보통신기술협회(TTA, 사무총장 임주환)는 지난 7월 8일 (주)래드비젼 (대표이사 안효진)의 외장형 ADSL모뎀(모델명 : LADNET-1000E)에 대하여 시험을 수행하여 TTA Tested 인증서(TTA-T-02-001)을 발급하였다. 이에 TTA는 래드비젼 ADSL 모뎀과 알카텔 7300 ASAM HD DSLAM과의 상호호환성 시험 및 중국 신식사업부에서 제안하고 있는 다양한 시험선로 환경 하에서의 데이터 전송 시험에 대한 시험결과를 소개하고자 한다.

• Journal of the Korean Society for Nondestructive Testing
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• v.23 no.1
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• pp.21-29
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• 2003

It is very important to reduce the computational time in estimating the parameters of vision control algorithm for robot's position control in real time. Unfortunately, the batch estimation commonly used requires too murk computational time because it is iteration method. So, the batch estimation has difficulty for robot's position control in real time. On the other hand, the Extended Kalman Filtering(EKF) has many advantages to calculate the parameters of vision system in that it is a simple and efficient recursive procedures. Thus, this study is to develop the EKF algorithm for the robot's vision control in real time. The vision system model used in this study involves six parameters to account for the inner(orientation, focal length etc) and outer (the relative location between robot and camera) parameters of camera. Then, EKF has been first applied to estimate these parameters, and then with these estimated parameters, also to estimate the robot's joint angles used for robot's operation. finally, the practicality of vision control scheme based on the EKF has been experimentally verified by performing the robot's position control.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1995.10a
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• pp.1124-1128
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• 1995

This paper presents the appplication of computer vision for the purpose of determing the position of the unknown point in the plane. The presented contrik method is estimate the six view parameters reqresenting the relationship between the image plane coordinates and the real physical coordinates. The estimation of six parameters is indispensable for transforming the 2-dimensional camera coordinates to the 3-dimensional spatial coordinates. Then, the position of unknown point is estimated based on the estimated parameters depending on the cameras. The suitability of this control scheme is demonstrated experimentally by determining of position the unknown point in the plane.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.6 no.2
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• pp.10-18
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• 1997

This paper presents the development of real-time estimation and control details for a computer vision-based robot control method. This is accomplished using a sequential estimation scheme that permits placement of these points in each of the two-dimensional image planes of monitoring cameras. Estimation model is developed based on a model that generalizes know 4-axis Scorbot manipulator kinematics to accommodate unknown relative camera position and orientation, etc. This model uses six uncertainty-of-view parameters estimated by the iteration method. The method is tested experimentally in two ways : First the validity of estimation model is tested by using the self-built test model. Second, the practicality of the presented control method is verified in performing 4-axis manipulator's assembly task. These results show that control scheme used is precise and robust. This feature can open the door to a range of application of multi-axis robot such as deburring and welding.

• Information and Communications Magazine
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• v.19 no.9
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• pp.86-98
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• 2002

• The Magazine of the IEIE
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• v.12 no.2
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• pp.53-56
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• 1985

• Proceedings of the IEEK Conference
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• 1998.06a
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• pp.879-882
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• 1998

본 논문에서는 리드 피치가 15mil인 208핀 QFP칩을 검사하는 고해상도의 비젼 모델 시스템을 개발하였다. 전체적인 하드웨어와 고해상도에 적합한 알고리즘 및 이를 실현하는 프로그램을 개발하였으며, 특히 칩 리드 검출을 위한 새로운 알고리즘을 제안하였다. 본 논문에서는 제안한 방법의 특징은 조명의 간섭에 대해 강하며 검사 속도 및 정밀도가 향상되었다.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.7 no.2
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• pp.62-68
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• 1998

This paper presents the application of computer vision for the purpose of determining the position of the unknown object in the plane. The presented control method is to estimate the six view parameters representing the relationship between the image plane coordinates and the real physical coordinates. The estimation of six parameters is indispensable for transforming the 2-dimensional camera coordinates to the 3-dimensional spatial coordinates. Then, the position of unknown point is estimated based on the estimated parameters depending on the cameras. The suitability of this control scheme is demonstrated experimentally by determining position of the unknown object in the plane.

• Journal of the Korean Society for Nondestructive Testing
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• v.25 no.2
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• pp.110-116
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• 2005

It is very important to reduce the computational processing time for the application of the vision system in real time such as inspection, the determination of object's dimension and welding etc, because the vision system model involves a lot of measurement data acquired by CCD camera. Also, a lot of computation time is required in estimating the parameters in the vision system model if the iterative batch estimation method such as Newton Raphson is used. Thus, the effective computation method such as the Extended Kalman Filtering(EKF) is required to solve the above problems. The EKF has much advantages in that it takes explicitly into account the measurement uncertainties, and is a simple and efficient recursive procedures. Thus, this study is to develop the EKF algorithm to compute the parameters in the vision system model in real time. This vision system model involves the six parameters to account for the cameras inner and outer parameters. Also the EKF is applied to estimate the object's dimension. Finally, practicality of the estimation scheme of the vision system based on the EKF is verified experimently by performing the estimation of object's dimension.