• Proceedings of the KIEE Conference
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• 1986.07a
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• pp.89-92
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• 1986

• Journal of Mechanical Science and Technology
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• v.20 no.7
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• pp.917-928
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• 2006

In recent decades, Artificial Neural Networks (ANNs) have become the focus of considerable attention in many disciplines, including robot control, where they can be used to solve nonlinear control problems. One of these ANNs applications is that of the inverse kinematic problem, which is important in robot path planning. In this paper, a neural network is employed to analyse of inverse kinematics of PUMA 560 type robot. The neural network is designed to find exact kinematics of the robot. The neural network is a feedforward neural network (FNN). The FNN is trained with different types of learning algorithm for designing exact inverse model of the robot. The Unimation PUMA 560 is a robot with six degrees of freedom and rotational joints. Inverse neural network model of the robot is trained with different learning algorithms for finding exact model of the robot. From the simulation results, the proposed neural network has superior performance for modelling complex robot's kinematics.

• International Journal of Precision Engineering and Manufacturing
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• v.8 no.4
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• pp.16-21
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• 2007

Robotic applications, such as automatic fish cutting, require online trajectory planning because the material properties of the object, such as the bone or flesh conditions, are not known in advance. Different trajectories are required when the material properties vary. An effective online trajectory-planning algorithm is proposed using quaternions to determine the position and orientation of a robot manipulator with a spherical wrist. Quaternions are free of representation singularities and permit computationally efficient orientation interpolations. To prevent singular configurations, the exact locations of the kinematic singularities of the PUMA 560 manipulator are derived and geometrically illustrated when a forearm offset exists and the third link length is not zero.

• 제어로봇시스템학회:학술대회논문집
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• 1987.10a
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• pp.892-897
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• 1987

The ability of a robotic manipulator to recognize the shape of an object by feeling its band around the object is useful in many applications. Two-dimensional object contour tracking by force feedback is described. The system consists of IBM PC/AT, PUMA 560 manipulator, PUMA controller and a tip sensor. Position control is accomplished by using VAL command and the unmodified PUMA controller. A contour tracking algorithm is developed and tested on three different types of objects. The experimental results show that the objects' shapes can be successfully identified.

• 제어로봇시스템학회:학술대회논문집
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• 1990.10b
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• pp.974-979
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• 1990

The system discussed in this paper is an integrated stand-alone system with the full functional capabilities required of a telerobot system. It is complete with a force-reflecting 6-DOF hand controller, driving a PUMA 560 or 762 robot, with an integrated force-torque sensing wrist sensor and servo-driven parallel jaw gripper. A mix of custom and standard electronics, distributed computers and microprocessors, with embedded and downloadable software, have been integrated into the system, giving rise to a powerful and flexible teleautonomous control system.

• International Journal of Control, Automation, and Systems
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• v.6 no.5
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• pp.702-712
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• 2008

This paper presents a novel approach for controlling electrically driven robot manipulators based on voltage control. The voltage-based control is preferred comparing to torque-based control. This approach is robust in the presence of manipulator uncertainties since it is free of the manipulator model. The control law is very simple, fast response, efficient, robust, and can be used for high-speed tracking purposes. The feedback linearization is applied on the electrical equations of the dc motors to cancel the current terms which transfer all manipulator dynamics to the electrical circuit of motor. The control system is simulated for position control of the PUMA 560 robot driven by permanent magnet dc motors.

• 제어로봇시스템학회:학술대회논문집
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• 1986.10a
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• pp.49-54
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• 1986

In this paper we deal with a method of controlling PUMA 560 robot manipulator using a newly developed optical proximity sensor and the PDP-11/44 computer. The sensor output is different somewhat depending on an color of the object. The range of sensing vary from 38.4mm to 109.5mm for a real object, 39mm to 111.65mm for yellow, and 40.55mm to 107.25mm for blue. When an obstacle is encountered on the path of end-effector the system acknowledger immediatly the existence of the obstacle : and holds the motion of arm at a given distance. And also the system is capable of making the end effector avoid the obstacle automatically and keep on its motion.

• Proceedings of the KIEE Conference
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• 1988.07a
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• pp.980-985
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• 1988

In this paper, a control system of the PUMA 560 robot manipulator using a PC (Personal Computer) is presented. The hardware of the designed control system is composed of IBM-PC/AT, interface board, selection board, interrupt generating circuit, and the servo control unit of the PUMA controller. A robot control library is developed using assembly and C language, and is composed of several low-level functions and arm interface routines, world model routines, arm kinematics routines, and motion command service routines. Using the designed library, joint interpolated motion and Cartesian interpolated motion of the PUMA robot manipulator are realized. In the future, our system is expected to be a very helpful basis and a useful tool for developing various control algorithms of robot manipulator using sensory information.

• Journal of the Korean Institute of Intelligent Systems
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• v.2 no.1
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• pp.17-32
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• 1992

The multi-layer neural network that has broadly been utilized in designing the controller of robot manipulator possesses the desirable characteristics of learning capacity, by which the uncertain variation of the dynamic parameters of robot can be handled adaptively, and parallel distributed processing that makes it possible to control on real-time. However the error back propagation algorithm that has been utilized popularly in the learning of the multi-layer neural network has the problem of its slow convergence speed. In this paper, an approach to improve the convergence speed is proposed using the fuzzy logic that can effectively handle the uncertain and fuzzy informations by linguistic level. The effectiveness of the proposed algorithm is demonstrated by computer simulation of PUMA 560 robot manupulator.

• Journal of the Korean Institute of Telematics and Electronics
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• v.26 no.10
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• pp.1471-1478
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• 1989

The optimal-time control of the coordinated motion of two robot manipulators may be of consequence in the industrial automation. In this paper two robot manipulators garsping a common object are assumed to travel a specified Cartesian path and the method how to derive the optimal-time solution is explained. This approach is based on parameterizing the corresponding patn and utilizing the phase-plame technique in the trajectory planning. Also the torques supplied by the actuators are assumed to have some constant bounds. The effectiveness of this approach is demonstrated by a computer simulation using a PUMA 560 manipulator.