• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.520-522
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• 1996

The Direct Drive Arm(DDA) is a SCARA typed direct drive manipulator with two degrees-of-freedom(DOF) using the direct drive motor of the NSK company. A controller system for the SCARA robot of DDA is designed using a DSP (TMS32Oc3O), which has the highest performance among the third DSP chips in the TI company. The design objective of the system is to implement dynamic control algorithms and neural control algorithms for real time learning which require a lot of calculations and large memory and have been tested only by simulations so far. The controller uses a DSP, a high speed D/A, 32-bit Counter and a large DRAM to implement advanced robot control algorithms.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1944-1948
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• 2005

This paper presents how it is effective to use many features for improving the speed and the accuracy of the visual servo systems. Some rank conditions which relate the image Jacobian and the control performance are derived. It is also proven that the accuracy is improved by increasing the number of features. Effectiveness of the redundant features is evaluated by the smallest singular value of the image Jacobian which is closely related to the accuracy with respect to the world coordinate system. Usefulness of the redundant features is verified by the real time experiments on a Dual-Arm Robot manipulator made in Samsung Electronic Co. Ltd

• Journal of Institute of Control, Robotics and Systems
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• v.15 no.8
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• pp.851-858
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• 2009

In this study, we propose a new input device using vision technology for positioning of multiple DOFs. The input device is composed of multiple Tags on a transparent table and a vision camera below the table. Vision camera detects LEDs at the bottom of each Tag to derive information of the ID, position and orientation. The information are used to determine position and orientation of remote target DOFs. Our developed approach is very reliable and effective, especially when the corresponding DOFs are from many independent individuals. We show an application example with a SCARA robot to prove the flexibility and extendability.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1996.04a
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• pp.472-477
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• 1996

This paper presents a new approachtothe design of adaptive control system using DSPs(TMS320C30) for robotic manipulators to achieve trajectory tracking by the joint angles. Digital signal processors are used in implementing real time adaptive control algorithms to provide an enhanced motion control for robotic manipulators. In the proposed control scheme, adaptation laws are derived from the improved Lyapunov second stability analysis method based on the adaptive model reference control theory. The adaptive controller consists of an adaaptive feedforward controller, feedback controller, and PID type time-varying auxillary control elements. The prpposed adaptive control scheme is simple in structure, fast in computation, and suitable for implementation of real-time control. Moreover, this scheme does not require an accurate dynamic modeling, nor values of manipulator parameters and payload. Performance of the adaptive controller is illustrated by simulation and experimental results for a SCARA robot.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.37 no.11
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• pp.796-803
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• 1988

In this paper, the design method, design techniques and structure of a language for a SCARA type industrial robot, are presented. The proposed new language is modular and expandable using the C programming language and the 8086 assembly language. It is composed of monitor mode which controls the main flow of the programs, editing mode which generates, corrects and edits the programs, execution mode which executes the generated programs, I/O mode which interacts with the external devices, and teach mode. The developed language is implemented on the robot controller to verify its performance.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2001.04a
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• pp.255-260
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• 2001

This paper presents a new approach to the design of neural control system using digital signal processors in order to improve the precision and robustness. Robotic manipulators have become increasingly important in the field of flexible automation. High speed and high-precision trajectory tracking are indispensable capabilities for their versatile application. The need to meet demanding control requirement in increasingly complex dynamical control systems under significant uncertainties, leads toward design of intelligent manipulation robots. The TMS320C31 is used in implementing real time neural control to provide an enhanced motion control for robotic manipulators. In this control scheme, the networks introduced are neural nets with dynamic neurons, whose dynamics are distributed over all the network nodes. The nets are trained by the distributed dynamic back propagation algorithm. The proposed neural network control scheme is simple in structure, fast in computation, and suitable for implementation of real-time control. Performance of the neural controller is illustrated by simulation and experimental results for a SCARA robot.

• Journal of the Korean Society for Precision Engineering
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• v.12 no.12
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• pp.165-172
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• 1995

Many algorithms to enhance a speed performance of a robot have been studied, but it's rare to consider disign aspect of a robot arm for time optimal problem. In this paper, section demensions of a robot arm and a velocity profile of an end-effector were optimally designed to minimize the cycle time. Capacity of actuators, deflections of end-effector, and a fundamental natural frequency of the robot arm were constrained in optimal design. For a given path with a trapezoidal velocity profile, torques of each joint were calculated using the inverse kinematics and dynamics. For the SCARA type robot which is mainly used for assembly tasks, the time optimal design of each robot arm id presented with the above constraints.

• 제어로봇시스템학회:학술대회논문집
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• 1990.10a
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• pp.493-496
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• 1990

Recently, the robot control systems are required to be more flexible and intelligent in order to execute more complex and delicate tasks. As an approach to such system, a PC-based robot control system is presented in this paper. The axis controllers are independently designed for each joint of robot manipulator and are supervised by a personal computer. Therefore, the almost system program including the control algorithm can be easily developed and modified in the PC's environment. This proposed control system was successfully applied to 4 axis SCARA-type robot and the associated S/W modules were developed. We can construct sensory robot systems by simply connecting the sensing devices to the PC and the study on this now being progressed.

• Journal of Mechanical Science and Technology
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• v.14 no.7
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• pp.782-788
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• 2000

In this paper, we present a new approach to visual feedback control using image-based visual servoing with stereo vision. In order to control the position and orientation of a robot with respect to an object, a new technique is proposed using binocular stereo vision. The stereo vision enables us to calculate an exact image Jacobian not only around a desired location but also at other locations. The suggested technique can guide a robot manipulator to the desired location without providing a priori knowledge such as the relative distance to the desired location or the model of an object even when the initial positioning error is large. This paper describes a model of stereo vision and how to generate feedback commands. The performance of the proposed visual servoing system is illustrated by experimental results and compared with conventional control methods for an assembly robot.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 1999.10a
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• pp.131-136
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• 1999

This paper proposes the robot inverse calibration method using a neural networks. A highorder networks called Pi-Sigma networks has been used. The Pi-Sigma networks uses linear summing units in the hidden layer and product unit in output layer. The inverse calibration model which compensates the difference of joint variables only between measuring value and analytic value about the desired pose(position, orientation) of a robot is proposed. The compensated values are determined by using the weights obtained from the learning process of the neural networks previously. To prove the reasonableness, the SCARA type direct drive robot(4-DOF) and anthropomorphic robot(6-DOF) are simulated. It shows that the proposed calibration method can reduce the errors of the joint variables from $\pm$2$^{\circ}$to $\pm$ 0.1$^{\circ}$.