• Journal of the Korea Society of Computer and Information
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• v.19 no.6
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• pp.139-149
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• 2014

Much research has been conducted in educational robot, a new instructional technology, for K- 12 education. Several studies have shown that educational robot provides effective learning opportunities for students in both content areas of STEM(science, technology, engineering, and mathematics) and critical academic skills, such as collaboration, problem solving and communication skills. However, most studies to date on applications of educational robots have been conducted outside the formal education setting. This study analyzed the influence of using robots in an elementary school science class in Korea with regard to science learning motivation. A total of 121 students in fourth and fifth grades participated in the study. The experimental group was taught using robots in the science class, while the control group was taught using traditional methods. Analysis of covariance (ANCOVA) was conducted to compare the between-group differences in learning motivation before and after the experiment; an interview was also conducted for the experimental group. The study results showed a significant improvement (p<.05) in both learning motivation in the experimental compared with the control group. There was also positive response to learning with a robot. This study will play an important role in research on the use of educational robot in formal education in the future.

• ICROS
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• v.12 no.1
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• pp.36-40
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• 2006

• 한국정보교육학회:학술대회논문집
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• 2010.01a
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• pp.337-342
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• 2010

로봇 프로그래밍 교육은 프로그래밍 교육의 문제점을 보완할 수 있는 대안으로 제시되었으나 아직 활성화되지는 못하고 있다. 로봇 프로그래밍 교육의 활성화를 위해서는 교수자 측면 뿐만 아니라 학습자 측면에서 그들에게 필요한 지원 도구가 개발되어야 한다. 이를 위해 본 연구에서는 학습자 측면을 고려한 로봇 프로그래밍 지원 도구를 설계하였다. 로봇 프로그래밍 지원 도구는 웹 기반으로 구축되며, 기본 예제 제공 및 우수 예제 선발 기능, 명령어 검색 및 자동완성 기능, 오류 위치 찾기 기능과 도움말, 질문하고 답하기 기능들을 제공하도록 설계하였다. 앞으로 설계된 내용을 바탕으로 로봇 프로그래밍 지원 도구를 개발하고 적용하여 그 효과를 검증해 볼 필요가 있다.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2002.05a
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• pp.28-31
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• 2002

본 논문에서는 자율 이동 로봇의 학습을 위해 신경망과 진화 알고리즘을 이용한 방법을 제안한다. 이것은 자연계의 생물이 진화와 학습을 통해 환경에 적응해 나가는 방식과 유사하다. 또한 본 논문에서는 행동기반 제어 방법인 포섭구조를 이용해 로봇의 행동을 제어하는 방법을 제안한다 포섭 구조는 행동 규칙을 병렬적으로 모듈화 하여 낮은 레벨에서는 기본적인 행동을 담당하고, 높은 레벨에서는 좀 더 복잡한 행동을 담당하는 구조로 되어있다 따라서 각 행동 레벨이 협조를 함으로써 복잡한 임무를 수행할 수 있다. 포섭 구조에서 각 레벨의 제어기는 신경 망으로 구성하며 각 행동 레벨이 서로 영향을 주고받으며 진화함으로써 주어진 임무를 달성하도록 한다. 제안된 방법은 자율 이동 로봇인 Khepera 로봇을 이용해 실제 환경에서 구현함으로서 그 유효성을 입증한다.

• Proceedings of the KIEE Conference
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• 2006.07d
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• pp.1921-1922
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• 2006

청소 로봇의 장애물 판단은 청소 로봇이 정확하고 빠르게 장애물을 파악하여 정밀한 제어를 수행하며 청소 효율을 향상 시키는데 중요하다. 청소 로봇이 장애물을 판단하는데 여러 가지 알고리즘이 있지만 신경망 알고리즘 특히, BP(Back-Propagation) 알고리즘을 적용하여 장애물 인식에 있어 반복학습 시키면 청소 로봇은 보다 빠르고 정착하게 장애물을 스스로 판단 할 수 있다. 본 논문에서는 청소 로봇에 부착된 초음파 센서와 장애물과의 거리데이터를 얻어, 이를 BP 알고리즘에 적용하는 것을 연구하며 학습률, 반복학습, 최대 제곱 오차값를 조정한 실험결과로 특성변화를 관찰하고 해석하여 검증한다.

• Proceedings of the KIEE Conference
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• 2000.07d
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• pp.2862-2864
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• 2000

네트워크 환경의 보편화와 함께 원격강의와 가상실험에 대한 연구가 진행되고 있다. 로봇공학교육에 있어서 가상현실은 가상실험을 구현하기 위한 밑바탕이 되며 값싸고 위험부담없이 실제실험과 유사한 환경을 구축하기 위한 도구가 된다. 본 논문에서는 월드와이트웹 상에서 동작가능하며 다양한 궤적과 제어기를 사용자가 조작하면서 비교 학습 할 수 있는 로봇제어 학습용 시뮬레이터를 개발한다. 가상로봇은 실제로봇과 유사한 특징을 가지며 게인값 설정에 따른 궤적추종을 로봇의 움직임으로 관찰할 수 있다.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.4
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• pp.457-467
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• 2004

In this paper, a robot is considered as an agent, a structure of robot is presented which consisted by multi-subagents and they have diverse capacity such as perception, intelligence, action etc., required for robot. Also, subagents are consisted by micro-agent($\mu$agent) charged for elementary action required. The structure of robot control have two sub-agents, the one is behavior based reactive controller and action selection sub agent, and action selection sub-agent select a action based on the high label action and high performance, and which have a learning mechanism based on the reinforcement learning. For presented robot structure, it is easy to give intelligence to each element of action and a new approach of multi robot control. Presented robot is simulated for two goals: chaotic exploration and obstacle avoidance, and fabricated by using 8bit microcontroller, and experimented.

• The Journal of Korean Association of Computer Education
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• v.15 no.5
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• pp.11-22
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• 2012

This study aimed to analyze applying TAM model the process that flow factors such as 'harmony of challenge and technology' exert effects on learners' attitudes of keeping learning in STEAM class employing robots. For the study, the 'Energy and Tools' chapter of the science textbook for the 6th grade's second semester was re-arranged, and applied for 189 students, and among them, only the 174 usable data were used for the analysis. As a result of analysis, students' learning immersion factor(factor of harmony of challenge and technology) had deeper effects on the factor of ease of learning than usefulness of learning and this in turn, had an effect on their intention to keep learning ultimately through the factor of value of learning as the study found. As a result of research, it was found that for indications identified, in order to use robots in STEAM class, for the students' intention to keep learning, it's essential for learners to have proper and active attitudes towards learning and basic knowledge of robots, and aspects of values should be considered that based on this, robot can assist in learning and affect results of learning in STEAM class. On the other hand, the factors of ease of learning and the combination of the challenge and technology do not gives direct (+) effect on the intention to continue learning and the value for learning, respectively. However, each of the two factor has indirect influence on each of the dependent variable within the significant range, which is the reason the author includes the result of the analysis.

• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.1
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• pp.95-100
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• 2021

This paper introduces model-based meta reinforcement learning as a control for the manipulator of an underwater construction robot. Model-based meta reinforcement learning updates the model fast using recent experience in a real application and transfers the model to model predictive control which computes control inputs of the manipulator to reach the target position. The simulation environment for model-based meta reinforcement learning is established using MuJoCo and Gazebo. The real environment of manipulator control for underwater construction robot is set to deal with model uncertainties.

• 대한공업교육학회지
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• v.33 no.2
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• pp.120-136
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• 2008

Using the tangible programming tools, which combines physical objects (e.g. robot) and educational programming language, may help to encourage learners' creative thinking as well as to enhance problem solving ability. That is, learners can have opportunities to simulate problem solving processes through the physical objects, such as robots. Therefore, they can minimize an fixation about problem solving process. These experience is effective to induce creative thinking that is useful to find new solutions and change environment actively. Therefore, we developed a robot based programming teaching and learning curriculum and implemented it in college level introductory programming courses. The result shows that the robot based programming learning has a positive effect in all three factors of learners' creative problem solving potential, especially in a cognitive factor. The cognitive factor includes general problem solving abilities as well as factors that explain creativity, such as divergent thinking, problem recognition, problem representation. These result means that the developed robot based programming teaching and learning curriculum give positive effect to creative problem solving abilities.