• 한국CDE학회논문집
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• 제19권4호
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• pp.356-367
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• 2014

A robotic sapatial augmented reality (RSAR) system, which combines robotic components with projector-based AR technique, is unique in its ability to expand the user interaction area by dynamically changing the position and orientation of a projector-camera unit (PCU). For a moving PCU mounted on a conventional robotic device, we can compute its extrinsic parameters using a robot kinematics method assuming a link and joint geometry is available. In a RSAR system based on user-created robot (UCR), however, it is difficult to calibrate or measure the geometric configuration, which limits to apply a conventional kinematics method. In this paper, we propose a data-driven kinematics control method for a UCR-based RSAR system. The proposed method utilized a pre-sampled data set of camera calibration acquired at sufficient instances of kinematics configurations in fixed joint domains. Then, the sampled set is compactly represented as a set of B-spline surfaces. The proposed method have merits in two folds. First, it does not require any kinematics model such as a link length or joint orientation. Secondly, the computation is simple since it just evaluates a several polynomials rather than relying on Jacobian computation. We describe the proposed method and demonstrates the results for an experimental RSAR system with a PCU on a simple pan-tilt arm.

• 예술인문사회 융합 멀티미디어 논문지
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• 제7권3호
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• pp.861-870
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• 2017

본 논문은 R-learning의 연장선에서 교육 환경 내의 소셜 로봇 도입과 함께 소셜 로봇의 역할에 대해 제안한다. 소셜 로봇은 개체 특히, 인간과의 소통과 상호작용을 목적으로 하는 로봇이다. 본 논문에서는 기존의 교육 환경에서 활용되었던 로봇의 역할은 물론, 소셜 로봇의 목적에 걸맞게 교육 환경 구성원과의 상호작용과 소통을 수행하는 역할을 제안한다. 이에 대한 설명을 돕기 위해 소셜 로봇 jibo의 SDK를 활용하여 간략한 가상 시나리오를 구현하여 예시로 보인다. 시나리오는 교육적 환경에서 이용자와의 가벼운 상호작용을 목적으로 제작되었으며, jibo SDK에서 jibo의 외적 반응을 제어하는 animation 파트와 내적인 반응을 제어하는 behaviors 파트를 활용하여 제작되었다. 교육 환경 내의 투입된 소셜 로봇은 구성원과의 상호작용 및 소통과 접목된 여러 기술들을 기반으로 환경 내의 데이터를 보다 효율적으로 수집한다. 결과적으로 이를 통해 교육 환경에 대한 데이터와 정보를 제공받아 교육환경 내의 문제점의 도출, 환경 자체의 발전 및 새로운 교육 환경의 설립에 긍정적 영향을 끼칠 수 있다.

• 제어로봇시스템학회논문지
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• 제20권1호
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• pp.58-69
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• 2014

This paper proposes a method for predicting maximum friction coefficients and optimal slip ratios as optimal control parameters for traction control or slip control of autonomous mobile robots on rough terrain. This paper focuses on strength of ground surface which indicates different characteristics depending on material types on surface. Strength of various material types can be estimated by Willoughby sinkage model and by a developed testbed which can measure forces, velocities, and displacements generated by wheel-terrain interaction. Estimated strength is collaborated on building improved Brixius model with friction-slip data from experiments with the testbed over sand and grass material. Improved Brixius model covers widespread material types in outdoor environments on predicting friction-slip characteristics depending on strength of ground surface. Thus, a prediction model for obtaining optimal control parameters is derived by partial differentiation of the improved Brixius model with respect to slip. This prediction model can be applied to autonomous mobile robots and finally gives secure maneuverability on rough terrain. Proposed method is verified by various experiments under similar conditions with the ones for real outdoor robots.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1991년도 한국자동제어학술회의논문집(국제학술편); KOEX, Seoul; 22-24 Oct. 1991
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• pp.1597-1600
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• 1991

In this paper, we describe a notion of sensor modeling method in multisensor data fusion using fuzzy set theory. Each sensor module is characterized by its fuzzy constraints to specific features of environment. These sensor fuzzy constraints can be imposed on multisensory data to verify their degree of truth and compatibility toward the final decision making. In comparison with other sensor modeling methods, such as probabilistic models or rule-based models, the proposed method is very simple and can be easily implemented in intelligent robot systems.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2003년도 학술회의 논문집 정보 및 제어부문 B
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• pp.777-780
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• 2003

We have tried to create a vision system like human eye for a long time. We have obtained some distinguished results through many studies. Stereo vision is the most similar to human eye among those. This is the process of recreating 3-D spatial information from a pair of 2-D images. In this paper, we have designed a stereo matching algorithm based on systolic array architecture using edges and pixel data. This is more advanced vision system that improves some problems of previous stereo vision systems. This decreases noise and improves matching rate using edges and pixel data and also improves processing speed using high integration one chip FPGA and compact modules. We can apply this to robot vision and automatic control vehicles and artificial satellites.

• Journal of information and communication convergence engineering
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• 제19권1호
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• pp.54-60
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• 2021

Various digital devices were supplied throughout the Fourth Industrial Revolution. Accordingly, the importance of data processing has increased. Data processing significantly affects equipment reliability. Thus, the importance of data processing has increased, and various studies have been conducted on this topic. This study proposes a modified Gaussian filter algorithm based on a fuzzy membership function. The proposed algorithm calculates the Gaussian filter weight considering the standard deviation of the filtering mask and computes an estimate according to the fuzzy membership function. The final output is calculated by adding or subtracting the Gaussian filter output and estimate. To evaluate the proposed algorithm, simulations were conducted using existing additive white Gaussian noise removal algorithms. The proposed algorithm was then analyzed by comparing the peak signal-to-noise ratio and differential image. The simulation results show that the proposed algorithm has superior noise reduction performance and improved performance compared to the existing method.

• 제어로봇시스템학회논문지
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• 제16권5호
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• pp.440-445
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• 2010

This paper proposes a single accelerometer sensor control algorithm to mobile inverted pendulum, generally called 'Segway', and evaluates the performance of this system comparing to the conventional ones. The commercialized 'Prototype Segway-PT' is initially considered as a next-generation transport vehicle. However, this robot is operated by three gyroscopes and two accelerometers to control the posture and speed, and it requires the complex signal processing for fusing the two sets of data. As the result of this, the growth rate of market size of 'Segway' is slow because of its high price mainly. In this paper, the mobile inverted pendulum is operated by a single accelerometer to simplify the control system to lower the price. Low pass filter is one of the good sensors to reducing the variation of an accelerometer, but it has time delay. This time delay disturbs real-time mobile inverted pendulum control. Like this, other various algorithms are used for this system, but each one has its own weak point. So this paper proposes a new filtering method, median filter and EKF. Median filter is used to image processing to reject impulse elements like salt and pepper noise. As the major performance evaluation parameter for the accelerometer, the high-frequency to low frequency ratio from FFT (Fast Fourier Transform) is used. Effectiveness of the proposed algorithms has been verified through the real experiments and the results are demonstrated.

• 제어로봇시스템학회논문지
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• 제17권1호
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• pp.22-30
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• 2011

Plane detection is very important information for mission-critical of robot in 3D environment. A representative method of plane detection is Hough-transformation. Hough-transformation is robust to noise and makes the accurate plane detection possible. But it demands excessive memory and takes too much processing time. Iterative randomized Hough-transformation has been proposed to overcome these shortcomings. This method doesn't vote all data. It votes only one value of the randomly selected data into the Hough parameter space. This value calculated the value of the parameter of the shape that we want to extract. In Hough parameters space, it is possible to detect accurate plane through detection of repetitive maximum value. A common problem in these methods is that it requires too much computational cost and large number of memory space to find the distribution of mixed multiple planes in parameter space. In this paper, we detect multiple planes only via data sampling using Self Organizing Map method. It does not use conventional methods that include transforming to Hough parameter space, voting and repetitive plane extraction. And it improves the reliability of plane detection through division area searching and planarity evaluation. The proposed method is more accurate and faster than the conventional methods which is demonstrated the experiments in various conditions.

• 한국인터넷방송통신학회논문지
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• 제16권5호
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• pp.105-110
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• 2016

현재, 국내 CNC 산업분야는 제조업의 패러다임 변화에 따라 공작기계에 첨단 기술의 적용이 활발하게 추진되고 있다. 즉, 공작기계의 제어를 담당하는 CNC의 IT화는 가장 중요한 부분을 차지하고 있다. 하지만 공작기계 분야에 모션제어 전문기술 부족의 가장 큰 이유는 PC기반의 컨트롤러의 경우 비중을 많이 차지하는 윈도우즈 언어를 사용하기 보다는 G-Code를 직접 좌표에 맞춰 코딩하거나 값비싼 외산 S/W를 통해 변환 하는 경우가 대부분이기 때문이다. 본 논문에서는 다양한 형태의 CAD 데이터를 손쉽게 G-Code 데이터로 변환할 수 있는 G-Code 변환 프로그램 및 CAD&CAM과 응용 소프트웨어를 개발하고 이 변환된 데이터를 바로 로봇을 통해 원하는 동작을 시킬 수 있는 전용 모션 컨트롤러를 구현한다.

• 대한전기학회논문지:시스템및제어부문D
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• 제53권2호
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• pp.127-134
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• 2004

In this paper, human motor behaving model based sensory motor coordination(SMC) algorithm is implemented on robotic grasping task. Compare to conventional SMC models which connect sensor to motor directly, the proposed method used biologically inspired human behaving system in conjunction with SMC algorithm for fast grasping force control of robot arm. To characterize various grasping objects, pressure sensors on hand gripper were used. Measured sensory data are simultaneously transferred to perceptual mechanism(PM) and long term memory(LTM), and then the sensory information is forwarded to the fastest channel among several information-processing flows in human motor system. In this model, two motor learning routes are proposed. One of the route uses PM and the other uses short term memory(STM) and LTM structure. Through motor learning procedure, successful information is transferred from STM to LTM. Also, LTM data are used for next moor plan as reference information. STM is designed to single layered perception neural network to generate fast motor plan and receive required data which comes from LTM. Experimental results showed that proposed method can control of the grasping force adaptable to various shapes and types of greasing objects, and also it showed quicker grasping-behavior lumining time compare to simple feedback system.