• Journal of Institute of Control, Robotics and Systems
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• v.20 no.6
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• pp.651-656
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• 2014

This paper presents a compensation method for an accelerometer to measure acceleration data accurately when a robot manipulator moves slowly. Although the accelerometer works fine under the fast movement of a robot manipulator, low cost accelerometers provide relatively inaccurate acceleration data under slow movements. In order to correct the error of the sensor data in the slow motion, correction factors are obtained experimentally. Then those corrected data are used for the disturbance observer. Experimental studies of the position control of a robot manipulator are conducted by applying the DOB (Disturbance Observer) control using corrected acceleration data.

• Journal of the Korean Institute of Intelligent Systems
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• v.8 no.5
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• pp.39-44
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• 1998

Determining the absolute location of a mobile robot is essential in the navigation of a mobile robot. In this paper, a method to determine the position of a mobile robot through the visual image of a landrnark using neural networks is proposed. In determining the position of a mobile robot on the world coordinate, there is a position error because of uncertainty in pixels, incorrect camera calibration and lens distortion. To reduce the errors, a method using a BPNN(Back Propagation Neural Network) is proposed. The experimental results are presented to illustrate the superiority of the proposed method when comparing with the conventional methods.

• Journal of Institute of Control, Robotics and Systems
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• v.9 no.7
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• pp.515-526
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• 2003

Tutorial contents of kinematics and dynamics of a wheeled drive mobile robot are presented. Based on the dynamic model, simulation studies of position tracking of a mobile robot are performed. The control structure of several position control algorithms using visual feedback are proposed and their performances are compared. In order to compensate for uncertainties from unknown dynamics and ignored dynamic effects such as slip conditions, neural network based position control schemes are proposed. Experiments are conducted and the results show the performance of the vision based neural network control scheme fumed out to be the best among several proposed schemes.

• Proceedings of the KIEE Conference
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• 2006.10c
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• pp.554-556
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• 2006

A key component for autonomous mobile robot is to localize ifself. In this paper we suggest a vision-based localization and compensation of robot's location using ultrasound. Mobile robot travels along wall and searches each feature in indoor environment and transformed absolute coordinates of actuality environment using these points and builds a map. And we obtain information of the environment because mobile robot travels along wall. Localzation search robot's location candidate point by ultrasound and decide position among candidate point by features matching.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.2271-2274
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• 2005

The desired ZMP is different from the actual ZMP of a humanoid robot during actual walking and stand upright. A humanoid robot must maintain its stable posture although external force is given to the robot. A humanoid robot can know its stability with ZMP. Actual ZMP may be moved out of the foot-print polygons by external disturbance or uneven ground surfaces. If the position of ZMP moves out of stable region, the stability can not be guaranteed. Therefore, The control of the ZMP is necessary. In this paper, ZMP control algorithm is proposed. Herein, the ZMP control uses difference between desired ZMP and actual ZMP. The proposed algorithm gives reaction moment with ankle joint when external force is supplied. 3D simulator shows motion of a humanoid robot and calculated data.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.249-249
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• 2000

This paper introduces a clean mobile robot fur 4th generation LCD cassette, which is guided by optical sensor and position compensation using vision module. The mobile robot for LCD cassette transfer might be controlled by AGV controller which has powerful algorithms. It offers optimum routes to the destination of clean mobile robot by using dynamic dispatch algorithm and MAP data. This clean mobile robot is equipped with 4 axes fork type manipulator providing repeatability accuracy of $\pm$ 0.05mm.

• Journal of Institute of Control, Robotics and Systems
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• v.11 no.6
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• pp.524-529
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• 2005

In this paper, a novel neural network compensation technique for PD like fuzzy controlled robot manipulators is presented. A standard PD-like fuzzy controller is designed and used as a main controller for controlling robot manipulators. A neural network controller is added to the reference trajectories to modify input error space so that the system is robust to any change in system parameter variations. It forms a neural-fuzzy control structure and used to compensate for nonlinear effects. The ultimate goal is same as that of the neuro-fuzzy control structure, but this proposed technique modifies the input error not the fuzzy rules. The proposed scheme is tested to control the position of the 3 degrees-of-freedom rotary robot manipulator. Performances are compared with that of other neural network control structure known as the feedback error learning structure that compensates at the control input level.

• International Journal of Control, Automation, and Systems
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• v.5 no.3
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• pp.343-348
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• 2007

This paper presents an adaptive controller for the compensation of state-dependent disturbance with unknown amplitude in a digital servo motor system. The state-dependent disturbance is caused by friction and eccentricity between the wheel axis and the motor driver of a mobile robot servo system. The proposed control scheme guarantees an asymptotical stability for both the velocity and position regulation. An experimental result shows the effectiveness of the adaptive disturbance compensator for wheeled-mobile robot in a low velocity diffusion tracking. A comparative experimental study with a simple PI controller is presented.

• Proceedings of the KIEE Conference
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• 2006.10c
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• pp.560-562
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• 2006

In this paper, Correct problem in dead reckoning system and proposed about position error revision techniques of mobile robot to use RFID tag for position awareness. With the dead reckoning system, as the accumulation of error are unavoidable because of accumulation of informations as the time passage, so it is impossible to get correct information about posture, including torrent direction, movement distance, etc. As one of compensation method, the suggested method is that after selecting special area (corridor), compensate absolute location information by arranging two line of RFID tag along two side of corridor. Through this suggested method, it could be used when robot wants to move in limited areas.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.30 no.7 s.250
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• pp.741-749
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• 2006

For last couple of decades, uses of TFI-LCDs have been expanded to many FPD(Flat Panel Display) applications including mobile displays, desktop monitors and TVs. Furthermore, there has been growing demand for increasingly larger LCD TVs. In order to meet this demand as well as to improve productivity, LCD manufactures have continued to install larger-generation display fabrication facilities which are capable of producing more panels and larger displays per mother glass(substrate). As the size of mother glass becomes larger, a robot required to handle the glass becomes bigger accordingly, and its end effectors(arms) are extended to match the glass size. With this configuration, a considerable static deflection occurs at the end of the robot arms. In order to stack maximum number of mother glasses on a given footprint, the static deflection should be compensated. This paper presents a novel static deflection compensation algorithm. This algorithm requires neither measurement instrument nor additional vertical axis on the robot. It is realized by robot controller software. The forward and inverse kinematics considering compensation always guarantees a unique solution, so the proposed algorithm can be applied to an arbitrary robot position. The algorithm reduced static deflection by 40% in stationary robot state experiment. It also improved vertical path accuracy up to 60% when the arm was running at its maximum speed. This algorithm has been commercialized and successfully applied to a seventh-generation LCD glass-handling robot.