• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1996년도 한국자동제어학술회의논문집(국내학술편); 포항공과대학교, 포항; 24-26 Oct. 1996
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• pp.229-232
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• 1996

The position control accuracy of the robot arm is decreased significantly when a long arm robot is operated at high speed. In this case, the robot arm must be modeled as a flexible structure, not a rigid one, and its control system will be necessarily designed with its elastic modes taken into account. In this paper, the vibration control of a one-link flexible robot arm is presented. The robot system consists of a flexible arm manufactured with thin aluminium plate, AC servomotor with a harmonic drive for speed reduction, optical encoder and accelerometer. The system is modeled with limited number of elastic modes, and its parameters are determined from the results of the experiments. The implemented control schemes are LQ control and sliding mode control. The experiments and digital simulations are carried out to test the validity of the system modeling, controller design, and active control implementation.

• 제어로봇시스템학회논문지
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• 제15권10호
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• pp.1014-1020
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• 2009

This work deals with navigation of an omni-directional mobile robot with active caster wheels. Initially, the posture of the omni-directional mobile robot is calculated by using the odometry information. Next, the position accuracy of the mobile robot is measured through comparison of the odometry information and the external sensor measurement. Finally, for successful navigation of the mobile robot, a motion planning algorithm that employs kinematic redundancy resolution method is proposed. Through experiments for multiple obstacles and multiple moving obstacles, the feasibility of the proposed navigation algorithm was verified.

• 제어로봇시스템학회논문지
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• 제18권12호
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• pp.1115-1121
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• 2012

This paper proposes a method to extract accurate plane of an object in unstructured environment for a humanoid robot by using a laser scanner. By panning and tilting 2D laser scanner installed on the head of a humanoid robot, 3D depth map of unstructured environment is generated. After generating the 3D depth map around a robot, the proposed plane extraction method is applied to the 3D depth map. By using the hierarchical clustering method, points on the same plane are extracted from the point cloud in the 3D depth map. After segmenting the plane from the point cloud, dimensions of the planes are calculated. The accuracy of the extracted plane is evaluated with experimental results, which show the effectiveness of the proposed method to extract planes around a humanoid robot in unstructured environment.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 1999년도 하계종합학술대회 논문집
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• pp.1113-1116
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• 1999

For mobile robot, the navigation effectiveness can be improved by providing autonomy, but this autonomy requires the mobile robot to detect unknown obstacles and avoid collisions while moving it toward the target. This paper presents an effective method for autonomous navigation of the mobile robot in structured environments. This method uses ultrasonic sensor array to detect obstacles and utilizes force relationship between the obstacles and the target for avoiding collisions. Accuracy of sensory data produced by ultrasonic sensors is improved by employing error eliminating rapid ultrasonic firing (EERUF) technique. Navigation algorithm controlling both the velocity and steering simultaneously is developed, implemented to the mobile robot and tested on the floor filled with the cluttered obstacles. It is verified that from the results of the field tests the mobile robot can move at a maximum speed of 0.66 m/sec without any collisions.

• 한국컴퓨터정보학회논문지
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• 제22권12호
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• pp.17-25
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• 2017

In this paper, we have implemented a low-cost mobile robot supporting the person tracking by detection using RGB-D cameras and ROS(Robot Operating System) framework. The mobile robot was developed based on the Kobuki mobile base equipped with 2's Kinect devices and a high performance controller. One kinect device was used to detect and track the single person among people in the constrained working area by combining point cloud data filtering & clustering, HOG classifier and Kalman Filter-based estimation successively, and the other to perform the SLAM-based navigation supported in ROS framework. In performance evaluation, the person tracking by detection was proved to be robustly executed in real-time, and the navigation function showed the accuracy with the mean distance error being lower than 50mm. The mobile robot implemented has a significance in using the open-source based, general-purpose and low-cost approach.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2007년도 심포지엄 논문집 정보 및 제어부문
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• pp.331-333
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• 2007

Robot manipulator is one of the important features in service robot area. Until now, there has been a lot of research on robot" manipulator that can imitate the functions of a human being by recognizing and grasping objects. In this paper, we present a robot arm based on the object recognition vision system. We have implemented closed-loop control that use the feedback from visual information, and used a sonar sensor to improve the accuracy. We have placed the web-camera on the top of the hand to recognize objects. We also present some vision-based manipulation issues and our system features.

• 로봇학회논문지
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• 제3권2호
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• pp.117-122
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• 2008

This paper presents a multi-robot localization based on multidimensional scaling (MDS) in spite of the existence of incomplete and noisy data. While the traditional algorithms for MDS work on the full-rank distance matrix, there might be many missing data in the real world due to occlusions. Moreover, it has no considerations to dealing with the uncertainty due to noisy observations. We propose a robust MDS to handle both the incomplete and noisy data, which is applied to solve the multi-robot localization problem. To deal with the incomplete data, we use the Nystr$\ddot{o}$m approximation which approximates the full distance matrix. To deal with the uncertainty, we formulate a Bayesian framework for MDS which finds the posterior of coordinates of objects by means of statistical inference. 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).

• Advances in robotics research
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• 제2권1호
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• pp.99-112
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• 2018

The main contribution of this work is the design of a field programmable gate array (FPGA) based ARX-Laguerre proportional-integral observation (PIO) system for fault detection and identification (FDI) in a multi-input, multi-output (MIMO) nonlinear uncertain dynamical robot manipulators. An ARX-Laguerre method was used in this study to dynamic modeling the robot manipulator in the presence of uncertainty and disturbance. To address the challenges of robustness, fault detection, isolation, and estimation the proposed FPGA-based PI observer was applied to the ARX-Laguerre robot model. The effectiveness and accuracy of FPGA based ARX-Laguerre PIO was tested by first three degrees of the freedom PUMA robot manipulator, yielding 6.3%, 10.73%, and 4.23%, average performance improvement for three types of faults (e.g., actuator fault, sensor faults, and composite fault), respectively.

• 대한산업공학회지
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• 제18권2호
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• pp.131-140
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• 1992

The purpose of this study is to improve the performance of robot by providing visual function with robot and developing high-functioned controller and control program. The developed high-functioned controller and software have the better accuracy and flexibility in the movement of robot by complementing the existing robot controller and software problems. To provide visual function to robot, camera calibration, thresholding and contouring tools are also developed and applied in this study. These tools help robot recognize the central points and orientations of objects on work-bench.

• 한국지능정보시스템학회:학술대회논문집
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• 한국지능정보시스템학회 2007년도 추계학술대회
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• pp.357-361
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• 2007

This paper presents a multi-robot localization based on Bayesian Multidimensional Scaling (BMDS). We propose a robust MDS to handle both the incomplete and noisy data, which is applied to solve the multi-robot localization problem. To deal with the incomplete data, we use the Nystr${\ddot{o}}$m approximation which approximates the full distance matrix. To deal with the uncertainty, we formulate a Bayesian framework for MDS which finds the posterior of coordinates of objects by means of statistical inference. 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).