• The Journal of Korea Robotics Society
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• v.15 no.1
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• pp.70-76
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• 2020

Multi-floor navigation of a mobile robot requires a technology that allows the robot to safely get on and off the elevator. Therefore, in this study, we propose a method of recognizing the elevator from the current position of the robot and estimating the location of the elevator locally so that the robot can safely get on the elevator regardless of the accumulated position error during autonomous navigation. The proposed method uses a deep learning-based image classifier to identify the elevator from the image information obtained from the RGB-D sensor and extract the boundary points between the elevator and the surrounding wall from the point cloud. This enables the robot to estimate the reliable position in real time and boarding direction for general elevators. Various experiments exhibit the effectiveness and accuracy of the proposed method.

• The Journal of Korea Robotics Society
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• v.2 no.1
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• pp.93-102
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• 2007

It is essential to estimating positions of multiple robots in order to perform cooperative task in common workspace. Accordingly, we propose a new approach of cooperative localization for multiple robots utilizing correlation among GPS errors in common workspace. Assuming that GPS data of individual robot are correlated strongly as the distance among robots are close, it is confirmed that the proposed method provides improved localization accuracy. In addition, we define two operational parameters to apply proposed method in multiple robot system. With mentioned two parameters, we present a practical solution to accumulated position error in traveling long distance.

• Journal of the Ergonomics Society of Korea
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• v.18 no.3
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• pp.127-140
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• 1999

In order to apply three-dimensional images to industries, the possibility of realizing three-dimensional images should be ensured and when operating a task using three-dimensional images, the intention of the observer and the result of operation should be precisely related. The aim of this paper is to investigate the task performance of a human operator during operating a robot manipulator using three-dimensional and two-dimensional image displays. From the result of this research, it was found that the accuracy of robot operation in the case of using three-dimensional displays is much higher than in the case of using two-dimensional displays and the adapting time to the operating task using three-dimensional displays is shorter than that using two-dimensional displays. From such results, we concluded that the application of three-dimensional displays, which can closely reflect real environment, to industries is desirable.

• Journal of Institute of Control, Robotics and Systems
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• v.11 no.1
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• pp.34-40
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• 2005

In this paper, we describe the modeling for the 3D robot laser scanning system consisting of a laser stripe projector, camera, and 5-DOF robot and propose its calibration method. Nonlinear radial distortion in the camera model is considered for improving the calibration accuracy. The 3D range data is calculated using the optical triangulation principle which uses the geometrical relationship between the camera and the laser stripe plane. For optimal estimation of the system model parameters, real-coded genetic algorithm is applied in the calibration process. Experimental results show that the constructed system is able to measure the 3D position within about 1mm error. The proposed scheme could be applied to the kinematically dissimilar robot system without losing the generality and has a potential for recognition for the unknown environment.

• Journal of Institute of Control, Robotics and Systems
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• v.15 no.8
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• pp.845-850
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• 2009

Localization which estimates a robot's position and orientation in a given environment is very important for mobile robot navigation. Although low-cost sensors are preferred for practical service robots, they suffer from the inaccurate and insufficient range information. This paper proposes a novel approach to increasing the success rate of low-cost sensor-based localization. In this paper, both the previous and the current data obtained from the IR sensors are used for localization in order to utilize as much environment information as possible without increasing the number of sensors. The sensor model used in the monte carlo localization (MCL) is modified so that the accumulated range information may be used to increase the accuracy in estimating the current robot pose. The experimental results show that the proposed method can robustly estimate the robot's pose in indoor environments with several similar places.

• Proceedings of the KIEE Conference
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• 2005.10b
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• pp.591-593
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• 2005

This paper presents the dynamic analysis of a humanoid robot using Nastran that is one of FEM analysis program. Generally, computer program based on the Lagrange-Euler method or Newton-Euler method was used for dynamic analysis of a robot. The Lagrange-Euler method requires much calculation performance and it is also hard to apply to complex structure, and the Newton-Euler method limits accurate modeling and calculation for closed structure like a humanoid robot. In this paper, mechanical and structural data are obtained from the Nastran. It is possible for Nastran to make model similar to real system and can apply a physical properties and laws to model. So, accurate simulation is possible. From this result, accurate data is gained by Nastran. Furthermore, this method is shown to be a useful method that guarantees accuracy for trajectory planning.

• Proceedings of the KIEE Conference
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• 2002.11c
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• pp.501-504
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• 2002

This research embodied DGPS (Differential GPS) system that robot detects users present position in outside environment as part of Lacteal gland robot that is sight obstacle. Therefore, introduced GPS system that is effective means that can save essential world coordinate to realize global navigation. However, it is no the effectiveness to use GPS that is having error of tens meter to apply to lacteal gland robot that is sight obstacle without revision. Therefore, this research embodied Localization system of lacteal gland robot that is sight obstacle using DGPS that make use of FM DARC system to use DGPS to heighten navigation accuracy of this.

• Journal of the Institute of Convergence Signal Processing
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• v.2 no.2
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• pp.89-94
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• 2001

The position control accuracy of a robot manipulator is significantly deteriorated when a long arm robot is operated at a high speed. This paper presents a very simple sliding mode control which eliminates multiple mode residual vibration in a robot manipulator. The neural network is used to avoid that sliding mode condition is deviated due to the change of system parameter and disturbance. This paper is suggested control system which designed by sliding mode controller using neural network. The effectiveness of proposed scheme is demonstrated through computer simulation.

• Proceedings of the Korean Information Science Society Conference
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• 2007.10c
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• pp.433-438
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• 2007

This paper presents a distance mapping-based localization method with incomplete data which means partially observed data. We make three contributions. First, we propose the use of Multi Dimensional Scaling (MDS) for multi-robot localization. Second, we formulate the problem to accomodate partial observations common in multi-robot settings. We solve the resulting optimization problem using #Scaling by Majorizing a Complicated function (SMACOF)#, a popular algorithm fur iterative MDS. Third, we not only verify the performance of MDS-based multi-robot localization by computer simulations, but also implement a real world localization of multi-robot team. Using extensive empirical results, we show that the accuracy of the proposed method is almost similar to that of Monte Carlo Localization(MCL).

• The Journal of Korea Robotics Society
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• v.2 no.3
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• pp.255-261
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• 2007

We propose a optimal fusion method for localization of multiple robots utilizing correlation between GPS on each robot in common workspace. Each mobile robot in group collects position data from each odometer and GPS receiver and shares the position data with other robots. Then each robot utilizes position data of other robot for obtaining more precise estimation of own position. Because GPS data errors in common workspace have a close correlation, they contribute to improve localization accuracy of all robots in group. In this paper, we simulate proposed optimal fusion method of odometer and GPS through virtual robots and position data.