• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2006.11a
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• pp.113-116
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• 2006

본 본문은 Arnold 방정식, Chua's 방정식과 같은 여러 종류의 카오스 회로를 이동 로봇에 내장하여 카오스 이동 로봇을 구성하고 이 카오스 이동 로봇이 어느 임의 평면을 카오스 궤적을 가지고 탐색하다가 목표물을 만나면 목표물을 집중 탐색하는 방법을 제시하고 그 결과를 검증 하였다. 목표물 탐색을 위해서 BVP 모델을 이용하여 목표물로 가정하여 카오스 제적을 가지고 집중적으로 탐색하도록 하는 알고리즘을 개발하고 그 결과를 검증하였으며 이에 대한 타당성을 확인하였다.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2006.11a
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• pp.119-122
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• 2006

본 본문은 Arnold 방정식, Chua's 방정식 같은 여러 종류의 카오스 회로를 이동 로봇에 내장하여 카오스 이동 로봇을 구성하고 이 카오스 이동 로봇이 어느 임의 평면을 카오스 궤적을 가지고 탐색하다가 장애물을 만나거나 근접하게 되면 장애물을 회피하는 방법을 제시하고 그 결과를 검증하였다. 장애물 회피를 위해서 장애물을 고정 장애물과 BVP 모델을 이용한 은닉 장애물로 장애물을 가정하여 카오스 제적을 가지고 회피하도록 하는 알고리즘을 개발하고 그 결과를 검증하였으며 이에 대한 타당성을 확인하였다.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2006.11a
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• pp.131-134
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• 2006

본 논문에서는 가정용 로봇 및 서비스 로봇과 같은 이동로봇에서 사용자의 감정을 인식하는 방법중 한가지인 얼굴영상을 이용한 감정인식 방법을 제안한다. 얼굴영상인식을 위하여 얼굴의 여러 가지 특징(눈썹, 눈, 코, 입)의 움직임 및 위치를 이용하며, 이동로봇에서 움직이는 사용자를 인식하기 위한 움직임 추적 알고리즘을 구현하고, 획득된 사용자의 영상에서 얼굴영역 검출 알고리즘을 사용하여 얼굴 영역을 제외한 손과 배경 영상의 피부색은 제거한다. 검출된 얼굴영역의 거리에 따른 영상 확대 및 축소, 얼굴 각도에 따른 영상 회전변환 등의 정규화 작업을 거친 후 이동 로봇에서는 항상 고정된 크기의 얼굴 영상을 획득 할 수 있도록 한다. 또한 기존의 특징점 추출이나 히스토그램을 이용한 감정인식 방법을 혼합하여 인간의 감성 인식 시스템을 모방한 로봇에서의 감정인식을 수행한다. 본 논문에서는 이러한 다중 특징점 추출 방식을 통하여 이동로봇에서의 얼굴 영상을 이용한 사용자의 감정인식 시스템을 제안한다.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.8 no.3
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• pp.220-224
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• 2008

This paper presents the polygon-based Q-leaning and Parallel SVM algorithm for object search with multiple robots. We organized an experimental environment with one hundred mobile robots, two hundred obstacles, and ten objects. Then we sent the robots to a hallway, where some obstacles were lying about, to search for a hidden object. In experiment, we used four different control methods: a random search, a fusion model with Distance-based action making (DBAM) and Area-based action making (ABAM) process to determine the next action of the robots, and hexagon-based Q-learning, and dodecagon-based Q-learning and parallel SVM algorithm to enhance the fusion model with Distance-based action making (DBAM) and Area-based action making (ABAM) process. In this paper, the result show that dodecagon-based Q-learning and parallel SVM algorithm is better than the other algorithm to tracking for object.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.4
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• pp.1824-1832
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• 2011

In this paper, a smart home service robot McBot II is newly developed in much more practical and intelligent system than McBot I which we had developed a few years ago. Thus far, vacuum-cleaners have lightened the burden of household chores but the operational labor that vacuum-cleaners entail has been very severe. Recently, a cleaning robot was commercialized to solve but it also was not successful because it still had the problem of mess-cleanup, which pertained to the clean-up of large trash and the arrangement of newspapers, clothes, etc. Hence, we develop a new home mess-cleanup robot McBot II to completely overcome this problem on real environments. The mechanical design and the basic control of McBot II, which performs mess-cleanup function etc. in house, is actually focused in this paper. McBot II is mechanically modeled in the same method that the human works in door by using the waist and the hands. The big-ranged vertical lift and the shoulder joints to be able to forward move are mechanically designed for the operating function as the human's waist when the robot works. The mobility of McBot II is designed in the holonomic mobile robot for the collision avoidance of obstacle and the high speed navigation on the small area in door. Finally, good performance of McBot II, which has been optimally desinged, is confirmed through the experimental results for the control of the robotic body, mobility, arms and hands in this paper.

• Structural Engineering and Mechanics
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• v.42 no.5
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• pp.747-760
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• 2012

In today's online social structure, people with electronic devices or network have been closely related to whether any of the activities, work, school, etc., is related to electronic devices, intelligent robot, and network control. The best mobility and the first rich media of these products as smart phones, smart phones rise rapidly in recent years, high speed processing performance and high free way to install software, deeply loved by many business people. However, not only for smart phone business aspects of the use, but also can engage in education of the teachers or the students are learning a great help. This study construct OCR-assisted learning software written by the JAVA made, and the installation is provided by the Android mobile phone users.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.30.2-30
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• 2001

As the era of the human friendly robot looms, the intelligent autonomous mobile robots have obtained tremendous interests in recent years. The robots may be service robots for serving human or industrial robots for replacing human. For the coexistance with human, the robots must be able to feel and recognize three dimensional space that human live. In this paper, we propose three dimensional environmental modeling method based on a neural network technique called Growing Neural Gas Network. The purpose of this neural network is to generate a graphical structure which reflects the topology of the input space. Through this method, the robots´ surroundings are autonomously segmented ...

• Journal of Convergence for Information Technology
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• v.10 no.4
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• pp.1-10
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• 2020

The performance of the personalized product recommendation system for offline shopping malls is poor compared with the one using online environment information since it is difficult to obtain visitors' characteristic information. In this paper, a mobile robot platform is suggested capable of recommending personalized advertisement using customers' sex and age information provided by Face API of MS Azure Cloud service. The performance of the developed robot is verified through locomotion experiments, and the performance of API used for our robot is tested using sampled images from open Asian FAce Dataset (AFAD). The developed robot could be effective in marketing by providing personalized advertisements at offline shopping malls.

• Journal of Institute of Control, Robotics and Systems
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• v.17 no.2
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• pp.157-163
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• 2011

This paper proposes a hybrid image stabilizing system which uses both optical image stabilizing system based on EKF (Extended Kalman Filter) and digital image stabilization based on SURF (Speeded Up Robust Feature). Though image information is one of the most efficient data for object recognition, it is susceptible to noise which results from internal vibration as well as external factors. The blurred image obtained by the camera mounted on a robot makes it difficult for the robot to recognize its environment. The proposed system estimates shaking angle through EKF based on the information from inclinometer and gyro sensor to stabilize the image. In addition, extracting the feature points around rotation axis using SURF which is robust to change in scale or rotation enhances processing speed by removing unnecessary operations using Hessian matrix. The experimental results using the proposed hybrid system shows its effectiveness in extended frequency range.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.10 no.2
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• pp.101-106
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• 2010

We propose an approach to estimate the real-time moving trajectory of an object in this paper. The object's position is obtained from the image data of a CCD camera, while a state estimator predicts the linear and angular velocities of the moving object. To overcome the uncertainties and noises residing in the input data, a Extended Kalman Filter(EKF) and neural networks are utilized cooperatively. Since the EKF needs to approximate a nonlinear system into a linear model in order to estimate the states, there still exist errors as well as uncertainties. To resolve this problem, in this approach the Kohonen networks, which have a high adaptability to the memory of the inputoutput relationship, are utilized for the nonlinear region. In addition to this, the Kohonen network, as a sort of neural network, can effectively adapt to the dynamic variations and become robust against noises. This approach is derived from the observation that the Kohonen network is a type of self-organized map and is spatially oriented, which makes it suitable for determining the trajectories of moving objects. The superiority of the proposed algorithm compared with the EKF is demonstrated through real experiments.