• 제어로봇시스템학회:학술대회논문집
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• 1987.10b
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• pp.32-35
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• 1987

An adaptive control scheme has been recognized as an effective approach for a robot manipulator to track a desired trajectory in spite of the presence of nonlinearties and parameter uncertainties in robot dynamic models. In this paper, an adaptive control scheme for a robot manipulator is proposed to design the self-tuning controller which combines the pole placement with the extended linearized perturbation model. And this control scheme has two components: a feadforward control and a feedback compensation control. Based on this, the controller is demonstrated by the simulation about position control of a three-link manipulator with payload and parameter uncertainty.

• 한국기계연구소 소보
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• s.19
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• pp.81-92
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• 1989

This paper presents a simple identification method of the actual kinematic parameters for the robot with parallel joints. It is known that Denavit-Hartenberg's coordinate system is not useful for nearly parallel joints. In this paper, the coordinate frames are reassigned to model the kinematic parameter between nearly parallel joints by four parameters. The proposed identification method uses a straight ruler about 1m long. A robot hand is placed by using a teaching pendant at the prescribed points on the ruler, and corresponding error function is defined. The identified kinematic parameters which make the error function zero are obtained by iterative least square error method based on the singular value decomposition. In the compensation of joint angles, only the position is considered because the usual applications of robot do not require a precise orientation control.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.14 no.1
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• pp.17-25
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• 2014

This paper presents the formulation of an impedance controller for regulating the contact force with the environment. To achieve an accurate force tracking control, uncertainties in both robot dynamics and the environment require to be addressed. As part of the framework of the proposed force tracking formulation, a neural network is introduced at the desired trajectory to compensate for all uncertainties in an on-line manner. Compensation at the input trajectory leads to a remarkable structural advantage in that no modifications of the internal force controllers are required. Minimizing the objective function of the training signal for a neural network satisfies the desired force tracking performance. A neural network actually compensates for uncertainties at the input trajectory level in an on-line fashion. Simulation results confirm the position and force tracking abilities of a robot manipulator.

• Journal of Institute of Control, Robotics and Systems
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• v.10 no.7
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• pp.625-633
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• 2004

In this paper, position control of a car-like mobile robot using a neural network is presented. positional information of the mobile robot is given by a laser range finder located remotely through wireless communication. The heading angle is measured by a gyro sensor. Considering these two sensor information as a reference, the robot posture is corrected by a cascaded controller. To improve the tracking performance, a neural network with a cascaded controller is used to compensate for any uncertainty in the robot. The neural network functions as a compensator to minimize the positional errors in on-line fashion. A car-like mobile robot is built as a test-bed and experimental studies of several controllers are conducted and compared. Experimental results show that the best position control performance can be achieved by a cascaded controller with a neural network.

• Proceedings of the KIEE Conference
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• 1996.07b
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• pp.1266-1268
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• 1996

In this study an automatic roiling-coli labeling system using robot vision system and peripheral mechanism is proposed and implemented, which instead of the manual labor to attach labels Rolling-coils in a steel miil. The binary image process for the image processing is performed with the threshold, and the contour line is converted to the binary gradient which detects the discontinuous variation of brightness of rolling-coils. The moment invariants algorithm proposed by Hu is used to make it easy to recognize even when the position of the center are different from the trained data. The position error compensation algorithm of six degrees of freedom industrial robot manipulator is also developed and the data of the position of the center rolling-coils, which is obtained by floor mount camera, are transfered by asynchronous communication method. Therefore even if the position of center is changed, robot moves to the position of center and performs the labeling work successfully. Therefore, this system can be improved the safety and efficiency.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.3 no.4
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• pp.73-80
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• 2004

A dual purpose robot automation system is developed for both arc welding and spot welding by one robot within a cell. The need for automation of both arc welding and spot welding processes is urgent while the production volume is not so big as to accommodate separate station for the two processes. Also, space is too narrow for separate station to be settled down in the factory. A spot welding robot is chosen and the function for arc welding are implemented in-house at cost of advanced functions. For the spot welding, a single pole type gun is used and the robot has to push down the plate to be welded, which causes the robot positioning error. Therefore, position error compensation algorithm is developed. The basic functions for the arc welding processes are implemented using the digital I/O board of robot controller, PLC, and A/D conversion PCB. The weaving pattern is taught in meticulously by manual teach. A fixture unit is also developed for dual purpose. The main aspects of the system is presented in this paper especially in the design and implementation procedure. The signal diagrams and sequence logic diagrams are also included. The outcome of the dual purpose welding cell is the increased productivity and good production stability which is indispensable for production volume prediction. Also, it leads to reduction of manufacturing lead time.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.658-663
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• 2005

This paper presents the steering control algorithm of a locomotion robot using a quaternion. The locomotion robot is to be moved on an irregular floor that can inevitably result in the errors of both position and orientation. Especially the orientation error should be compensated every work in order to adjust the misaligned values of current orientation to those commanded values. In this paper, we propose a new steering control algorithm between the two values by using a quaternion with spherical cubic interpolation. The proposed algorithm is shown to be effective in terms of vibration when compared to a conventional simple compensation without interpolation, by using $MATLAB^{(R)}$ and $VisualNastran4D^{(R)}$.

• Proceedings of the KIEE Conference
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• 1992.07a
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• pp.411-413
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• 1992

This paper proposes a simple and high performance hybrid position/force control of robots based on disturbance compensation by using the disturbance observer in task coordinate space. The disturbance observer linealizes system of robot manipulators in task coordinate space and realizes acceleration control. To realize the strict acceleration control, the disturbance observer whose input is a position signal by simple computation, works as if it were a disturbance detector. The inverse kinematics can be simplified, because the disturbance observer in task coordinate space compensates not only the disturbance but also the error due to the simplification of the inverse kinematics. The new strategy is applied to a three-degrees-of freedom direct drive robot. The robust and simple hybrid position/force control is realized experimentally.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.1099-1104
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• 2003

In this paper, the position control of a car-like mobile robot using neural network is proposed. The positional information of the mobile robot is given by a laser range finder located remotely through wireless communication. The heading angle is measured by a gyro sensor. Considering these two sensor information as references, the robot posture by localization is corrected by a cascaded controller. In order to improve the tracking performance, a neural network with a cascaded controller is used to compensate for any uncertainty in the robot. The remotely located neural network filter modifies the reference trajectories to minimize the positional errors by wireless communication. A car-like mobile robot is built as a test-bed and experimental studies of proposed several control algorithms are performed. It turns out that the best position control can be achieved by a cascaded controller with neural network.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.49 no.5
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• pp.257-263
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• 2000

One of the basic assumptions in the static gait design for a walking robot is that the weight of leg should be negligible compared to that of body, so that the total gravity center is not affected by swing of a leg. Based on the ideal assumption of zero leg-weight, conventional static gait has been simply designed for the gravity center of body to be inside the support polygon, consisting of each support leg's tip position. In case that the weight of leg is relatively heavy, however, while the gravity center of body is kept inside the support polygon, the total gravity center of walking robot can be out of the polygon due to weight of a swinging leg, which causes instability in walking. Thus, it is necessary in the static gait design of a real robot a compensation scheme for the fluctuation in the gravity center. In this paper, a body impedance control is proposed to obtain the total gravity center based on foot forces measured from load cells of a real walking robot and to adjust its position to track the pre-designed trajectory of the corresponding ideal robot's body center. Therefore, the walking stability is secured even in case that the weight of leg has serious influence on the total gravity center of robot.