• 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.

• 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.

• 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.

• 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.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1995.10a
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• pp.1114-1119
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• 1995

This paper presents the development of estimation model and control method based on the new computer vision. This proposed control method is accomplished using a sequential estimation scheme that permits placement of the rigid body in each of the two-dimensional image planes of monitoring cameras. Estimation model with six parameters is developed based on a model that generalizes known 4-axis scara robot kinematics to accommodate unknown relative camera position and orientation, etc. Based on the estimated parameters,depending on each camers the joint angle of robot is estimated by the iteration method. The method is tested experimentally in two ways, the estimation model test and a three-dimensional rigid body placement task. Three 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 assembly and welding.

• Journal of the Korean Society for Precision Engineering
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• v.13 no.8
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• pp.175-183
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• 1996

This paper presents three-dimensional robot task using the vision control method. A minimum of two cameras is required to place points on end dffectors of n degree-of-freedom manipulators relative to other bodies. 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 known three-axis manipulator kinematics to accommodate unknown relative camera position and orientation, etc. This model uses six uncertainty-of-view parameters estimated by the iteration method.

• Journal of the Korean Society for Precision Engineering
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• v.15 no.11
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• pp.100-107
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• 1998

This paper presents the development of estimation model and control method based on the new robot vision. This proposed control method is accomplished using the sequential estimation scheme that permits placement of the rigid body in each of the two-dimensional image planes of monitoring cameras. Estimation model with six parameters is developed based on the model that generalizes known 4-axis scara robot kinematics to accommodate unknown relative camera position and orientation, etc. Based on the estimated parameters, depending on each camera the joint angle of robot is estimated by the iteration method. The method is experimentally tested in two ways, the estimation model test and a three-dimensional rigid body placement task. Three 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 assembly and welding.

• 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.

• Proceedings of the KIEE Conference
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• 2008.10b
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• pp.294-295
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• 2008

본 논문에서는 검사 및 측정용 비젼 시스템에서 카메라와 평면거울을 이용하여 영상을 촬영할 패 발생하는 왜곡의 보정 방법을 제안한다. 빛이 거울과 카메라 사이를 통과할 때 거울 위치의 틀어짐으로 인하여 직진성을 보장받기 어렵다. 이러한 문제점을 해결하기 위하여 왜곡 영상에 대한 모델을 구하고 이를 해결하는 방법을 제시한다. 거울의 위치의 틀어짐은 영상 촬영시 일그러진 사각형 형태로 왜곡이 나타난다. 이 특성을 이용하여 사각형 형태의 실험 대상체를 생성하여 이를 촬영하고 왜곡의 정도를 측정하여 평면거울의 보상값을 구한다. 이 방법으로 평면거울의 자세를 보정하여 왜곡되지 않은 영상을 얻을 수 있다.

• Proceedings of the Korean Information Science Society Conference
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• 1998.10c
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• pp.594-596
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• 1998

본 논문에서는 컴퓨터 비젼의 사진측량학적 방법과 대상물의 기하학적 정보를 결합하여, 사진으로부터 건축물의 정확한 값을 복원해 내는 새로운 모델링 시스템을 소개한다. 사용자의 간단한 프로토타입핑으로 얻은 대략의 모델 정보를 시스템이 사진 이미지와의 오차를 최소화하는 방법으로 자동적으로 모델 정보를 복원해 내며, 사진에서 최선의 텍스쳐 맵을 생성해 내므로, 세밀한 모델링 없이도 사진 수준의 사실적인(photo-realistic)모델을 얻을 수 있다. 빠르고 간단한 방법으로 실재 대상의 효과적인 모델을 얻을 수 있는 이 연구의 성과는 가상현실 분야의 모델링 작업에서 추구하는 바와 일치하므로, 가상현실 분야에서 유용하게 사용될 것으로 보이며, 알맞은 대상에 적용을 통해 실질적인 응용이 기대되어진다.