• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.4
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• pp.451-456
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• 2005

In this paper, we propose the navigation control method for intelligent robot using messy genetic algorithm. The fuzzy controller design for navigation of the intelligent robot was dependant on expert's knowledge. But, the parameters of the fuzzy logic controller obtained from expert's control action may not be outimal. In this paper, to solve the above problem, we propose the identification method to automatically tune the number of fuzzy rule and parameters of memberships of fuzzy controller using mGA. Finally, to show and evaluate the generality and feasibility of the proposed method, we provides some simulations for wall following navigation of intelligent robot.

• Journal of Institute of Control, Robotics and Systems
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• v.22 no.6
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• pp.389-396
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• 2016

The sealing robot must be able to calculate the slope of a contact surface for complete adherence of the sealing on different concrete shapes. After the slope is obtained, the robot will track on the surface of the concrete, but this process contains an error in the actual purpose of the force command. The reason this a phenomenon occurs, the non-linearity of the contact surface and the end-effector, is due to parasitic coupling. Errors like make it difficult to measure accurately the respective factors. Therefore, it is regarded as a disturbance that occurs when it follows the work surface it. In this paper, we selected the friction coefficient of the surface as a control factor and designed a compensator to reduce effects of disturbance. Finally, in view of the non-linearity of the end-effector of a robot to contact surfaces directly, we propose a robust force tracking controller in the finite range for managing disturbances that occur during the sealing.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.147.1-147
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• 2001

Future robots, especially service and personal robots, will need much more intelligence, robustness and user-friendliness. The ability to learn contributes to these characteristics and is, therefore, becoming more and more important. Three of the numerous varieties of learning are discussed together with results of real-world experiments with three autonomous robots: (1) the acquisition of map knowledge by a mobile robot, allowing it to navigate in a network of corridors, (2) the acquisition of motion control knowledge by a calibration-free manipulator, allowing it to gain task-related experience and improve its manipulation skills while it is working, and (3) the ability to learn how to perform service tasks ...

• Transactions of the Korean Society of Mechanical Engineers A
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• v.21 no.11
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• pp.1819-1826
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• 1997

Direct-drive robots are suitable to the position and force control with high accuracy, but it is difficult to design a controller because of the system's nonlinearity and link-interactions. This paper is concerned with the study of the force control of direct-drive robots. The proposed algorithm consists of feedback controllers and a neural network. After the completion of learning, the output of feedback controller is nearly equal to zero, and the neural network controller plays an important role in the control system. Therefore, the optimum retuning of parameters of feedback controllers is unnecessary. In other words, the proposed algorithm does not require any knowledge of the controlled system in advance. The effectiveness of the proposed algorithm is demonstrated by the experiment on the force control of the parallelogram link-type direct-drive robot.

• Journal of Advanced Marine Engineering and Technology
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• v.21 no.3
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• pp.284-292
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• 1997

This paper is concerned with position control of direct drive robots. The proposed algorithm consists of the feedback controller and neural networks. Mter the completion of learning, the output of the feedback controller is nearly equal to zero, and the neural networks play an important role in the control system. Therefore, the optimum retuning of control parameters is unnecessary. In other words, the proposed algorithm does not need any knowledge of the con¬trolled system in advance. The effectiveness of the proposed algorithm is demonstrated by the experiment on the position control of a parallelogram link-type direct drive robot.

• Journal of Industrial Technology
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• v.16
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• pp.113-121
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• 1996

In the early developmental stages of robotics,hydraulics played an important role. As the power-to-weight ratio of electric motors increased, they eventually replaced hydraulic actuators in robot manipulators. Recently, however, task requirements have dictated that the manipulator payload capacity increase to accomodate greater payload, greater length, greater reaction forces, and hydraulic actusators are being studied as an effective form of robot actuation again. For efficient control of hydraulic actuators, the knowledge of its dynamic equation is essential. However, the dynamic equation of hydraulic actuators are nonlinear, and the dynamic coefficients are time varying. In this paper, an estimation algorithm of the dynamic coefficients of the hydraulic piston dynamics are formulated. Simulation results are presented to show the possibility of the parameter estimation.

• 제어로봇시스템학회:학술대회논문집
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• 1990.10b
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• pp.1173-1177
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• 1990

Cerebellar Model Linear Associator Net(CMLAN), a kind of neuro-net based adaptive control function generator, was applied to the problem of direct inverse calibration of three and six d.o.f. POMA 560 robot. Since CMLAN autonomously maps and generalizes a desired system function via learning on the sampled input/output pair nodes, CMLAN allows no knowledge in system modeling and other error sources. The CMLAN based direct inverse calibration avoids the complex procedure of identifying various system parameters such as geometric(kinematic) or nongeometric(dynamic) ones and generates the corresponding desired compensated joint commands directly to each joint for given target commands in the world coordinate. The generated net outputs automatically handles the effect of unknown system parameters and dynamic error sources. On-line sequential learning on the prespecified sampled nodes requires only the measurement of the corresponding tool tip locations for three d.o.f. manipulator but location and orientation for six d.o.f. manipulator. The proposed calibration procedure can be applied to any robot.

• Journal of Advanced Marine Engineering and Technology
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• v.21 no.4
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• pp.404-413
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• 1997

Direct-drive robots are suitable to position and force control with high accuracy, but it is difficult to design a controller which gives satisfactory perfonnance because of the system's nonlinearity and link-interactions. This paper is concerned with the force control of direct-drive robots. The pro¬posed algorithm consists of feedback controllers and a neural network. Mter the completion of learning, the outputs of feedback controllers are nearly equal to zero, and the neural network con¬troller plays an important role in the control system. Therefore, the optimum adjustment of parameters of feedback controllers is unnecessary. In other words, the proposed algorithm does not need any knowledge of the controlled system in advance. The effectiveness of the proposed algo¬rithm is demonstrated by the experiment on the force control of a parallelogram link-type direct¬drive robot.

• Proceedings of the KIEE Conference
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• 1998.11b
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• pp.514-516
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• 1998

A Mobile Robot should be able to navigate safely in the workspaces without any additional human's helps. In this paper, a method to generate the safe path to avoid the unknown obstacles using the pre-knowledge of the workspaces was proposed. For the efficiency of the algorithm, it is proposed to model the obstacles as the line segments in numerical map, which can reduce the required memory size and give the simple forms. To make the environments map, we used the Hough transform and the sonar measurements is converted to the set of line segments by Hough transform. In this algorithm, the subgoals are generated to avoid the obstacles until a mobile robot arrives the final position using the proposed environmental model.

• Journal of Institute of Control, Robotics and Systems
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• v.7 no.12
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• pp.980-984
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• 2001

When robot manipulatros as mathematically modeled. uncetainties may not be avoided. The uncertain factors come from imperfect knowledge of system parameters, payload change. friction, external disturbance and etc. In this work, we proposed a class of robust hybrid control of manipulatosrs. We propose a class of expanded robust hybrid control with the separated bound function and the simulation results are provided to show the effectiveness of the algorithm.