• Journal of the Korean Institute of Telematics and Electronics
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• v.27 no.9
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• pp.1364-1371
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• 1990

We propose a time-optimal path planning scheme for the robot manipulator using Hopfield neural network. The time-optimal path planning, which can allow the robot system to perform the demanded tasks with a minimum execution time, may be of consequence to improve the productivity. But most of the methods proposed till now suffers from a significant computational burden and thus limits the on-line application. One way to avoid such a difficulty is to apply the neural networke technique, which can allow the parallel computation, to the minimum time problem. This paper proposes an approach for solving the time-optimal path planning by using Hopfield neural network. The effectiveness of the proposed method is demonstrarted using a PUMA 560 manipulator.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.28B no.11
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• pp.934-940
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• 1991

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 convergencs speed. In this paper, an approach to improve the convergence speed is proposed using 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 manipulator.

• Proceedings of the KIEE Conference
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• 1998.07g
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• pp.2312-2314
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• 1998

To improve the visual servoing performance, several strategies were proposed in the past such as redundant feature points, using a point with different height and weighted selection of image features. The performance of these visual servoing methods depends on the configuration between the camera and object. And redundant feature points require much computation efforts. This paper proposes the visual servoing m based on the disturbance observer, which compe the upper off-diagonal component of image fe Jacobian to be null. The performance indices su sensitivity for a measure of richness, sensitiv the control to noise, and controllability are sho improved when the image feature Jacobian is giv a block diagonal matrix. Computer simulation carried out for a PUMA560 robot and show results to verify the effectiveness of the pro method.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.29B no.10
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• pp.27-36
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• 1992

This paper proposes a numerical method of motion planning for manipulators using Foruier series. For a redundant manipulator, we predetermine the trajectories of redundant joints in terms of the Nth partial sum of the fourier series. then the optimal coefficients of the fourier series are searched by the Powell's method. For a nonredundant or redundant manipulator, CS02T-continuous smooth joint trajectory for a point-to-point task can be obtained while considering the frequency response. We apply the proposed method to the 3-link planar manipulator and the PUMA 560 manipulator. To show the validity of the proposed method, we analyze solutions by the Fast Fourier Transform (FFT). Also, several features are discussed to obtain an optimal solution.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.40 no.2
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• pp.207-216
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• 1991

In the case that the robot manipulator should respond to the variance and uncertainty of the environment in performing preforming precision tasks, it is indispensable that the robot utilizes the various sensors for intrlligence. In this paper, the robot force control method is implemented with a force/torque sensor, two personal computers, and a PUMA 560 manipulator for performing the various application tadks. The hybrid position/force control method is used to control the force and position axis separately. An interface board is designed to read the force/torque sensor output into the computer. Since the two computers should exchange the information quickly, a common memory board is designed. Before the algorithms of application tasks are developed, the basic force commands must be supplied. Thus, the MOVE-UNTIL command is used at the discrete time instant and, the MOVE-COMPLY is used at the continuous time instant for receiving the force feedback information. Using the two basic force commands, three application algorithms are developed and implemented for edge-following, insertion, and grinding tasks.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.25 no.6
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• pp.504-510
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• 2016

Robots are widely utilized in many fields, and various demands need customized robots. This study proposes an optimal design method based on swarm intelligence for selecting the kinematic parameter of a manipulator according to the task space trajectory desired by the user. The optimal design method is dealt with herein as an optimization problem. This study is based on swarm intelligence-based optimization algorithms (i.e., ant colony optimization (ACO) and particle swarm optimization algorithms) to determine the optimal kinematic parameters of the manipulator. The former is used to select the optimal kinematic parameter values, whereas the latter is utilized to solve the inverse kinematic problem when the ACO determines the parameter values. This study solves a design problem with the PUMA 560 when the desired task space trajectory is given and discusses its results in the simulation part to verify the performance of the proposed design.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.8
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• pp.2529-2551
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• 2022

Visual servoing (VS) based on the Kalman filter (KF) algorithm, as in the case of KF-based image-based visual servoing (IBVS) systems, suffers from three problems in uncalibrated environments: the perturbation noises of the robot system, error of noise statistics, and slow convergence. To solve these three problems, we use an IBVS based on KF, African vultures optimization algorithm enhanced extreme learning machine (AVOA-ELM), and fuzzy logic (FL) in this paper. Firstly, KF online estimation of the Jacobian matrix. We propose an AVOA-ELM error compensation model to compensate for the sub-optimal estimation of the KF to solve the problems of disturbance noises and noise statistics error. Next, an FL controller is designed for gain adaptation. This approach addresses the problem of the slow convergence of the IBVS system with the KF. Then, we propose a visual servoing scheme combining FL and KF-AVOA-ELM (FL-KF-AVOA-ELM). Finally, we verify the algorithm on the 6-DOF robotic manipulator PUMA 560. Compared with the existing methods, our algorithm can solve the three problems mentioned above without camera parameters, robot kinematics model, and target depth information. We also compared the proposed method with other KF-based IBVS methods under different disturbance noise environments. And the proposed method achieves the best results under the three evaluation metrics.