• 전자공학회논문지S
• /
• 제34S권8호
• /
• pp.56-64
• /
• 1997

In distributed autonomous robotic systems, each robot must behaves by itself according to the its states and environements, and if necessary, must cooperates with other orbots in order to carray out a given task. Therefore it is essential that each robot has both learning and evolution ability to adapt the dynamic environments. In this paper, the new learning and evolution method based on reinforement learning having delayed reward ability and distributed genectic algorithms is proposed for behavior learning and evolution of collective autonomous mobile robots. Reinforement learning having delayed reward is still useful even though when there is no immediate reward. And by distributed genetic algorithm exchanging the chromosome acquired under different environments by communication each robot can improve its behavior ability. Specially, in order to improve the perfodrmance of evolution, selective crossover using the characteristic of reinforcement learning is adopted in this paper, we verify the effectiveness of the proposed method by applying it to cooperative search problem.

• 정보교육학회논문지
• /
• 제11권2호
• /
• pp.195-203
• /
• 2007

유비쿼터스 컴퓨팅 기술과 로봇 기술의 발달과 함께 지능형 로봇은 여러 분야에서 활용되고 있고, 점차 그 범위가 확대될 것으로 예견된다. 많은 서비스 로봇들 중에서 교육용 로봇을 이용한 r-Learning의 개념과 함께 다양한 필드 스터디가 이루어지고 있다. 현재 교사 보조 로봇은 곧 실용화를 앞두고 많은 HRI 연구가 요구되는데, 본 연구에서는 로봇의 교수 스타일에 따라 학생의 반응을 살펴보고자 하였다. 이를 위하여 초등학교 6학년 평균키의 양팔이 달린 교사보조 로봇 프로토 타입을 제작하고, 동일한 영어단원에 대하여 두 가지 교수 스타일(명랑, 진지)의 컨텐츠를 개발하여 탑재한 후 초등학생 3학년을 대상으로 흥미도, 성취도, 집중도가 어떻게 다른지를 실험 비교하였다. 실험 결과, 학생의 흥미도는 명랑한 로봇과 함께 수업한 집단이 높았지만, 성취도는 로봇의 스타일과 유의미한 관계가 없었으며, 집중도는 진지한 교수 스타일의 로봇과 함께한 그룹의 시간이 길었다. 이러한 결과는 교사보조 로봇의 컨텐츠를 제작함에 있어, 중요한 가이드라인이 될 것이다.

• 한국지능시스템학회논문지
• /
• 제17권5호
• /
• pp.591-597
• /
• 2007

군집 로봇시스템에서 개개의 로봇은 스스로 주위의 환경과 자신의 상태를 스스로 판단하여 행동하고, 필요에 따라서는 다른 로봇과 협조를 통하여 어떤 주어진 일을 수행할 수 있어야 한다. 따라서 개개의 로봇은 동적으로 변화하는 환경에 잘 적응할 수 있는 학습과 진화능력을 갖는 것이 필수적이다 이를 위하여 본 논문에서는 새로운 Polygon 기반의 Q-learning 알고리즘과 분산유전알고리즘을 이용한 새로운 자율이동로봇의 행동학습 및 진화방법을 제안한다. 또한 개개의 로봇이 통신을 통하여 염색체를 교환하는 분산유전알고리즘은 각기 다른 환경에서 학습한 우수한 염색체로부터 자신의 능력을 향상시킨다. 특히 본 논문에서는 진화의 성능을 향상시키기 위하여 강화학습의 특성을 이용한 선택 교배방법을 채택하였다. 제안된 방법은 협조탐색 문제에 적용하여 컴퓨터 모의실험을 통하여 그 유효성을 검증한다.

• 컴퓨터교육학회논문지
• /
• 제12권4호
• /
• pp.13-21
• /
• 2009

교육용 로봇은 학습자들에게 흥미를 제공하고, 참여와 동기를 증진시킨다는 점에서 많은 잠재력을 가지고 있지만, 로봇의 사용이 반드시 효과적이고 성공적인 학습을 이끄는 것은 아니므로, 로봇 프로그래밍 교수 학습 환경과 학습 참가자들의 특성을 고려한 교수 학습 설계가 필요하다. 따라서 본 연구에서는 전문계 고등학교 학습자의 동기를 유발하고 지속시키기 위한 로봇 프로그래밍 교수학습 모형을 개발하였다. 먼저, 선행 연구 문헌 분석을 통해 로봇 프로그래밍 교수 학습 환경과 전문계 고등학교 학습자들의 특성을 파악하였다. 이러한 특성을 기반으로 델파이 연구를 진행하여 동기부여 전략을 추출하고 적용 방법을 개발하였다. 전문계 고등학교 학습자들을 위하여 개발된 교수학습 모형은 5개 구성요소 및 21개 전략을 포함한다.

• 동력기계공학회지
• /
• 제2권3호
• /
• pp.74-82
• /
• 1998

This paper presents a new approach to the design of speed and azimuth control of a mobile robot with two drive wheels. The proposed control scheme uses a Gaussian function as a unit function in the neural-fuzzy network and back propagation algorithm to train the neural-fuzzy network controller in the framework of the specialized learning architecture. It is proposed to a learned controller with two neural-fuzzy networks based on an independent reasoning and a connection net with fixed weights to simplify the neural-fuzzy network. The performance of the proposed controller can be seen by the computer simulation for trajectory tracking of the speed and azimuth of a mobile robot driven by two independent wheels.

• 반도체디스플레이기술학회지
• /
• 제20권3호
• /
• pp.140-145
• /
• 2021

This paper deals with the development of a deep-learning-based runway cleaning robot using an optical camera. A suitable model to realize real-time object detection was investigated, and the differences between the selected YOLOv3 and other deep learning models were analyzed. In order to check whether the proposed system is applicable to the actual runway, an experiment was conducted by making a prototype of the robot and a runway model. As a result, it was confirmed that the robot was well developed because the detection rate of FOD (Foreign Object Debris) and cracks was high, and the collection of foreign substances was carried out smoothly.

• 한국공작기계학회:학술대회논문집
• /
• 한국공작기계학회 2002년도 추계학술대회 논문집
• /
• pp.235-240
• /
• 2002

This paper presents a new approach to the design of cruise control system of a mobile robot with two drive wheel. The proposed control scheme uses a Gaussian function as a unit function in the fuzzy neural network, and back propagation algorithm to train the fuzzy neural network controller in the framework of the specialized learning architecture. It is proposed a learning controller consisting of two neural network-fuzzy based on independent reasoning and a connection net with fixed weights to simply the neural networks-fuzzy. The performance of the proposed controller is shown by performing the computer simulation for trajectory tracking of the speed and azimuth of a mobile robot driven by two independent wheels.

• 제어로봇시스템학회:학술대회논문집
• /
• 제어로봇시스템학회 2000년도 제15차 학술회의논문집
• /
• pp.431-431
• /
• 2000

The modeling of 5-bar linkage robot manipulator dynamics by means of a mathematical and neural architecture is presented. Such a model is applicable to the design of a feedforward controller or adjustment of controller parameters. The inverse model consists of two parts: a mathematical part and a compensation part. In the mathematical part, the subsystems of a 5-bar linkage robot manipulator are constructed by applying Kawato's Feedback-Error-Learning method, and trained by given training data. In the compensation part, MLP backpropagation algorithm is used to compensate the unmodeled dynamics. The forward model is realized from the inverse model using the inverse of inertia matrix and the compensation torque is decoupled in the input torque of the forward model. This scheme can use tile mathematical knowledge of the robot manipulator and analogize the robot characteristics. It is shown that the model is reasonable to be used for design and initial gain tuning of a controller.

• 제어로봇시스템학회논문지
• /
• 제8권7호
• /
• pp.607-612
• /
• 2002

An automatic design method of behaviors in behavior-based robotics is proposed. With this method, a robot can design its behaviors by itself without aids of human designer. Automating design procedure of behaviors can make the human designer free from somewhat tedious endeavor that requires to predict all possible situations in which the robot will work and to design a suitable behavior for each situation. A simple reinforcement learning strategy is the main frame of this method and the key parameter of the learning process is significant change of reward value. A successful application to mobile robot navigation is reported too.

• 로봇학회논문지
• /
• 제18권3호
• /
• pp.352-357
• /
• 2023

Since robotic assembly in an unstructured environment is very difficult with existing control methods, studies using artificial intelligence such as reinforcement learning have been conducted. However, since long-time operation of a robot for learning in the real environment adversely affects the robot, so a method to shorten the learning time is needed. To this end, a method based on a pre-trained neural network was proposed in this study. This method showed a learning speed about 3 times than the existing methods, and the stability of reward during learning was also increased. Furthermore, it can generate a more optimal policy than not using a pre-trained neural network. Using the proposed reinforcement learning-based assembly trajectory generator, 100 attempts were made to assemble the power connector within a random error of 4.53 mm in width and 3.13 mm in length, resulting in 100 successes.