• Proceedings of the Korean Society of Precision Engineering Conference
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• 2005.06a
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• pp.1060-1067
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• 2005

This paper attempts to derive and analyze the dynamic system of pinching a rigid object by means of two multi-degrees-of-freedom robot fingers with soft and deformable tips. It is shown firstly that a set of differential equation describing dynamics system of the manipulators and object together with geometric constraint of tight area-contacts is formulated by Lagrange's equation. It is shown secondly that the problems of controlling both the forces of pressing object and the rotation angle of the object under the geometric constraints are discussed. In this paper, the control method for dynamic stable grasping and enhancing dexterity in manipulating things is proposed. It is illustrated by computer simulation that the control system gives the performance improvement in the dynamic stable grasping of the dual fingers robot with soft tips.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.3
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• pp.203-211
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• 2013

This paper presents the implementation and control of two arms of an indoor service robot for floor tasks. The robot arms are designed to have 6 degrees-of-freedom (DOF), but actually built to have 5 DOF. Forward and inverse kinematics of two arms are analyzed and simulated to confirm the kinematic analysis. Two arms are actually controlled based on the inverse kinematics. The right and left arms are separately controlled to follow different trajectories in order to make sure the functionality of both arms. Experimental studies are conducted to confirm the kinematic analysis and proper operation of two arms.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.14 no.4
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• pp.81-88
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• 2005

This paper attempts to derive the dynamic model of handling tasks in finger robot which grasps stable and manipulates a rigid object with some dexterity. Firstly, a set of differential equation describing dynamics of the manipulators and object together with geometric constraint of tight area-contacts is formulated by Lagrange's equation. Secondly, the roblems of controlling both the forces of pressing object and the rotation angle of the object under the geometric constraints are discussed. The effect of geometric constraints of area-contacts between the link's end-effector and the object is analyzed and the model based on the differential-algebraic equations is presented. In this paper, the control method for dynamic stable grasping and enhancing dexterity in manipulating things is proposed. It is illustrated by computer simulation and the experiment that the control system gives the performance improvement in the dynamic stable grasping and nimble manipulating of the dual fingers robot with soft tips.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.20 no.12
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• pp.3871-3882
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• 1996

In this paper, a closed-form formulation for inverse kinematics of robot manipulators with kinematic redundancy under the constrained environment has been derived using the Kuhn-Tucker condition, the extended Lagrange multiplier method and the working set method. The proposed algorithm satisfies the necessaryand sufficient conditions for optimization subject to equality and inequality constraints. In addition, computationally efficient kinematic control methods have been proposed using differential kinemetics and gradient projection mehtod. The effectiveness of the proposed methods has been demonstrated with a 4-dof planar robot, and then a 7-dof spatial robot as a practical application to the nozzle dam task in the Nuclear Power Plant.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.34S no.7
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• pp.48-55
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• 1997

An adaptive output feedback controller is designed for tracking control of an n-link robot manipulator with unknown load. High-gain obwserver that is used to estimate joint velocities is designed to avoide the restriction of the allowable variation range of unknown parmeters as well as improve the state estimation error. We saturate the control inut outside a domain of interest and use an adaptive law with a parameter projection feature to guarantee boundedness of all the trajectories in the closed-loop system. Simulation resutls on a 2-link manipulator illustrate that when the speed of the high-gain observer is sufficiently high, the proposed controller recovers the performance under state feedback control.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2002.04a
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• pp.393-398
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• 2002

Neural networks are used in the framework of sensorbased tracking control of robot manipulators. They learn by practice movements the relationship between PSD (an analog Position Sensitive Detector) sensor readings for target positions and the joint commands to reach them. Using this configuration, the system can track or follow a moving or stationary object in real time. forthermore, an efficient neural network architecture has been developed for real time learning. This network uses multiple sets of simple backpropagation networks one of which is selected according to which division (corresponding to a cluster of the self-organizing feature map) in data space the current input data belongs to. This lends itself to a very training and processing implementation required for real time control.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10a
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• pp.660-665
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• 1993

Neural networks are used in the framework of sensorbased tracking control of robot manipulators. They learn by practice movements the relationship between PSD ( an analog Position Sensitive Detector) sensor readings for target positions and the joint commands to reach them. Using this configuration, the system can track or follow a moving or stationary object in real time. Furthermore, an efficient neural network architecture has been developed for real time learning. This network uses multiple sets of simple backpropagation networks one of which is selected according to which division (corresponding to a cluster of the self-organizing feature map) in data space the current input data belongs to. This lends itself to a very fast training and processing implementation required for real time control.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2001.04a
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• pp.239-243
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• 2001

Neural networks are used in the framework of sensorbased tracking control of robot manipulators. They learn by practice movements the relationship between PSD (an analog Position Sensitive Detector) sensor readings for target positions and the joint commands to reach them. Using this configuration, the system can track or follow a moving or stationary object in real time. Furthermore, an efficient neural network architecture has been developed for real time learning. This network uses multiple sets of simple backpropagation networks one of which is selected according to which division (corresponding to a cluster of the self-organizing feature map) in data space the current input data belongs to. This lends itself to a very training and processing implementation required for real time control.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2000.04a
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• pp.171-175
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• 2000

Neural networks are in the framework of sensorbased tracking control of robot manipulators. They learn by practice movements the relationship between PSD (an analog Position Sensitive Detector) sensor readings for target positions and the joint commands to reach them. Using this configuration, the system can track or follow a moving or stationary object in real time. Furthermore, an efficient neural network architecture has been developed for real time learning. This network uses multiple sets of simple backpropagation networks one of which is selected according to which division (corresponding to a cluster of the self-organizing feature map) in data space the current input data belongs to. This lends itself to a very training and processing implementation required for real time control.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2000.10a
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• pp.108-112
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• 2000

Neural networks are used in the framework of sensorbased tracking control of robot manipulators. They learn by practice movements the relationship between PSD (an analog Position Sensitive Detector) sensor readings for target positions and the joint commands to reach them. Using this configuration, the system can track or follow a moving or stationary object in real time. Furthermore, an efficient neural network architecture has been developed for real time learning. This network uses multiple sets of simple backpropagation networks one of which is selected according to which division (corresponding to a cluster of the self-organizing feature map) in data space the current input data belongs to. This lends itself to a very training and processing implementation required for real time control.