• Asian Journal of Innovation and Policy
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• 제8권1호
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• pp.73-96
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• 2019

This study suggests a new approach for identifying core robot tech-nologies based on technological cross-impact. Specifically, the approach applies data mining techniques and multi-criteria decision-making methods to the co-classification information of registered patents on the robots. First, a cross-impact matrix is constructed with the confidence values by applying association rule mining (ARM) to the co-classification information of patents. Analytic network process (ANP) is applied to the co-classification frequency matrix for deriving weights of each robot technology. Then, a technique for order performance by similarity to ideal solution (TOPSIS) is employed to the derived cross-impact matrix and weights for identifying core robot technologies from the overall cross-impact perspective. It is expected that the proposed approach could help robot technology managers to formulate strategy and policy for technology planning of robot area.

• 전기학회논문지
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• 제67권8호
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• pp.1089-1098
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• 2018

In this paper, we propose an autonomous feeding robot and its obstacle classification system using ultrasonic sensors to secure the driving safety of the robot and efficient feeding operation. The developed feeding robot is verified by operation experiments in a cattle shed. In the proposed classification algorithm, not only the maximum amplitude of the ultrasonic echo signal but also two gradients of the signal and the variation of amplitude are considered as the feature parameters for object classification. The experimental results show the efficiency of the proposed classification method based on the Support Vector Machine, which is able to classify objects or obstacles such as a human, a cow, a fence and a wall.

• 로봇학회논문지
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• 제17권4호
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• pp.463-476
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• 2022

In this paper, we propose a table tennis posture classification system using a cooperative robot to develop a table tennis robot that can be trained like a real game. The most ideal table tennis robot would be a robot with a high joint driving speed and a high degree of freedom. Therefore, in this paper, we intend to use a cooperative robot with sufficient degrees of freedom to develop a robot that can be trained like a real game. However, cooperative robots have the disadvantage of slow joint driving speed. These shortcomings are expected to be overcome through quick recognition. Therefore, in this paper, we try to quickly classify the opponent's posture to overcome the slow joint driving speed. To this end, learning about dynamic postures was conducted using image data as input, and finally, three classification models were created and comparative experiments and evaluations were performed on the designated dynamic postures. In conclusion, comparative experimental data demonstrate the highest classification accuracy and fastest classification speed in classification models using MLP (Multi-Layer Perceptron), and thus demonstrate the validity of the proposed algorithm.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2010년도 춘계학술대회 논문집
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• pp.755-756
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• 2010

• 제어로봇시스템학회논문지
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• 제19권11호
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• pp.1043-1047
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• 2013

Smart mobile robot is a kind of Intelligent Robot. It means that operates manipulate autonomously and recognize the external environment. Smart mobile robot moving mechanism has many type and the type depend on the robot shape or purpose. Recently, research on the moving mechanism has been actively in many area. The moving mechanism divided to wheel type, crawler type, walking type, other type and the moving type choose by the kind of robot or the purpose robot. In this paper, describe the kind of moving mechanism on the smart mobile robot and the technical trend of moving mechanism of smart mobile robot.

• 전기학회논문지
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• 제59권12호
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• pp.2314-2318
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• 2010

For human-robot interaction, a robot should recognize the meaning of human behavior. In the case of static behavior such as face expression and sign language, the information contained in a single image is sufficient to deliver the meaning to the robot. In the case of dynamic behavior such as gestures, however, the information of sequential images is required. This paper proposes behavior classification by using fuzzy classifier to deliver the meaning of dynamic behavior to the robot. The proposed method extracts feature points from input images by a skeleton model, generates a vector space from a differential image of the extracted feature points, and uses this information as the learning data for fuzzy classifier. Finally, we show the effectiveness and the feasibility of the proposed method through experiments.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2010년도 춘계학술대회 논문집
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• pp.377-378
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• 2010

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제5권4호
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• pp.302-309
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• 2005

We have studied a model and application that evolves the behavioral knowledge of a virtual robot. The knowledge is represented in classification rules and a neural network, and is learned by a genetic algorithm. The model consists of a virtual robot with behavior knowledge, an environment that it moves in, and an evolution performer that includes a genetic algorithm. We have also applied our model to an environment where the robots gather food into a nest. When comparing our model with the conventional method on various test cases, our model showed superior overall learning.

• 한국지능시스템학회논문지
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• 제25권3호
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• pp.254-259
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• 2015

본 논문은 BCI(Brain Computer Interface)기반의 정상상태시각유발전위(SSVEP : Steady-State Visual Evoked Potential)를 사용하여 무선 로봇 제어를 위한 시스템을 제안하였다. CPSD(Cross Power Spectral Density)를 사용하여 전극의 신호를 분석하였다. 또한 분류를 위해서 LDA(Linear Discriminant Analysis)와 SVM(Support Vector Machine)을 사용하였다. 그 결과 피험자들의 평균 분류율은 약 70%로 나타났다. 로봇제어의 경우 뇌파의 값을 분류하여 나타난 결과 값으로 로봇이 움직일 수 있도록 구현하였고, 블루투스 통신을 이용하여 로봇제어를 수행하였다.

• 로봇학회논문지
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• 제3권3호
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• pp.165-175
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• 2008

For an advanced intelligent service, the need of HRI technology has recently been increasing and the technology has been also improved. However, HRI components have been evaluated under stable and controlled laboratory environments and there are no evaluation results of performance in real environments. Therefore, robot service providers and users have not been getting sufficient information on the level of current HRI technology. In this paper, we provide the evaluation results of the performance of the HRI components on the robot platforms providing actual services in pilot service sites. For the evaluation, we select face detection component, speaker gender classification component and sound localization component as representative HRI components closing to the commercialization. The goal of this paper is to provide valuable information and reference performance on appling the HRI components to real robot environments.