• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.1820-1823
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• 1997

For the Cartesian space position controlled robot, it is required to have the accurate mapping from the Cartesian space to the joint space in order to command the desired joint trajectories correctly. since the actual mapping from Cartesian space to joint space is obtained at the joint coordinate not at the actuator coordinate, uncertainty in Jacobian can be present. In this paper, two feasible neural network schemes are proposed to compensate for the kinematic Jacobian uncertainties. Uncertainties in Jacobian can be compensated by identifying either actuator Jacobian off-line or the inverse of that in on-line fashion. the case study of the stenciling robot is examined.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2000.11a
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• pp.647-652
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• 2000

This paper presents a study on the position tracking control of robot system on the uncertain elastic base. The elastic base is modeled as a virtual robot which has passive joints and the control strategy is using approximate Jacobian operators. Jacobian operators represent the overall robot system including base movement. However, because we don't know the base movement we can't estimate the jacobian operators directly. The control algorithm is proposed which uses only Jacobian operators of a real robot as approximate Jacobian operators. The measured errors from external sensor are compensated by approximate Jacobian operators. The simulation results of a single-axis robot system show that the control strategy can be used for position tracking.

• Journal of the Korean Society for Precision Engineering
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• v.18 no.10
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• pp.45-52
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• 2001

This paper presents a study on the position tracking control of a robot system on the uncertain elastic base. The elastic bathe is a nonholonomic system but it can be changed into holonomic system, which is much easier to analyze, by modeling an elastic base as a virtual robot that has passive joints. Also, Jacobian operators, which represent the overall robot system including base movement, are defined and applied to the changed model. However, because base movements are not known, the exact Jacobian operators can＇t be estimated. The control algorithm proposed is that uses only Jacobians of a real robot as approximate Jacobian operators. Therefore the approximate Jacobian operators compensate the measured errors from external sensors. The proposed control strategy is evaluated by the simulation and experiment of a single-axis robot system on the elastic base.

• Journal of Advanced Navigation Technology
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• v.10 no.2
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• pp.113-120
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• 2006

In this paper, a vision-based robust controller for tracking the desired trajectory a robot manipulator is proposed. The trajectory is generated to move the feature point into the desired position which the robot follows to reach to the desired position. To compensate the parametric uncertainties of the robot manipulator which contain in the control input, the robust controller is proposed. In addition, if there are uncertainties in the Jacobian, to compensate it, a vision-based robust controller which has control input is proposed as well in this paper. The stability of the closed-loop system is shown by Lyapunov method. The performance of the proposed method is demonstrated by simulations and experiments on a two degree of freedom 5-link robot manipulators.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.1816-1819
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• 1997

Using the vision system, robotic tasks in unstructured environments can be accompished, which reduces greatly the cost and steup time for the robotic system to fit to he well-defined and structured working environments. This paper proposes a dynamic control scheme for robot manipulator with eye-in-hand camera configuration. To perfom the tasks defined in the image plane, the camera motion Jacobian (image Jacobian) matrix is used to transform the camera motion to the objection position change. In addition, the dynamic learning controller is designed to improve the tracking performance of robotic system. the proposed control scheme is implemented for tasks of tracking moving objects and shown to outperform the conventional visual servo system in convergence and robustness to parameter uncertainty, disturbances, low sampling rate, etc.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.33B no.11
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• pp.11-21
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• 1996

Robot manipulators constituing multi-robot system must exert the desired motion force on an object to preserve tghe fine motion of it. The forces exerte on an object by the end-effectors of multi-inducing force and the internal force. Here, motion-inducing force effects the motion of an object, but internal force as lies in the null space of an object coordinate can't effect it. The motion of an object can't track exactly the desired motion by the effect of an object, but internal force as lies in the null space of the effect of internal force component, therefore internal force component must be considered. In this paper, first, under assumption that we can estimate exactly the parameter of dynamics, we constitute paper, first, under assumption that we can estimate exactly the parameter of dynamics, we constitute the controller concerning internal force. And we obtain the internal force as projecting force sensor readings onto the space spanned by null basis set of jacobian matrix. Using the resolved acceleration control method and the fact that internal force lies in the null space of jacobian matrix, we construct the robust control law to preserve the robustness with respect to the uncertainty of mainpulator parameters.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.539-542
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• 1996

The forces exerted on an object by the end-effectors of multi-manipulators are decomposed into the motion-inducing force and the internal force. Motion-inducing force effects the motion of an object and internal force can't effect it. The motion of an object can't track exactly the desired motion because of internal force component, therefore internal force component must be considered. In this paper using the resolved acceleration control method and the fact that internal force lies in the null space of jacobian matrix, we construct independently the position, motion-inducing force and internal force controller. Secondly we construct the robust controller to preserve the robustness with respect to the uncertainty of manipulator parameters.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.49 no.1
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• pp.41-47
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• 2000

A better tracking performance can be achieved, if visual sensors such as CCD cameras are used in controling a robot manipulator, than when only relative sensors such as encoders are used. However, for precise visual servoing of a robot manipulator, an expensive vision system which has fast sampling rate must be used. Moreover, even if a fast vision system is implemented for visual servoing, one cannot get a reliable performance without use of robust and stable inner joint servo-loop. In this paper, we propose a dynamic control scheme for robot manipulators with eye-in-hand camera configuration, where a dynamic learning controller is designed to improve the tracking performance of robotic system. The proposed control scheme is implemented for tasks of tracking moving objects and shown to be robust to parameter uncertainty, disturbances, low sampling rate, etc.

• IEIE Transactions on Smart Processing and Computing
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• v.4 no.4
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• pp.216-223
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• 2015

The autonomous underwater vehicle (AUV) is being widely researched in order to achieve superior performance when working in hazardous environments. This research focuses on using image processing techniques to estimate the AUV's egomotion and the changes in orientation, based on image frames from different time frames captured from a single high-definition web camera attached to the bottom of the AUV. A visual odometry application is integrated with other sensors. An internal measurement unit (IMU) sensor is used to determine a correct set of answers corresponding to a homography motion equation. A pressure sensor is used to resolve image scale ambiguity. Uncertainty estimation is computed to correct drift that occurs in the system by using a Jacobian method, singular value decomposition, and backward and forward error propagation.