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

• 한국멀티미디어학회논문지
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• 제25권8호
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• pp.1049-1058
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• 2022

HAR(Human Action Recognition) such as anomaly and object detection has become a trend in research field(s) that focus on utilizing Artificial Intelligence (AI) methods to analyze patterns of human action in crime-ridden area(s), media services, and industrial facilities. Especially, in real-time system(s) using video streaming data, HAR has become a more important AI-based research field in application development and many different research fields using HAR have currently been developed and improved. In this paper, we propose and analyze a deep-learning-based HAR that provides more efficient scheme(s) using an intelligent AI models, such system can be applied to media services using RGB video streaming data usage without feature extraction pre-processing. For the method, we adopt Slowfast based on the Deep Neural Network(DNN) model under an open dataset(HMDB-51 or UCF101) for improvement in prediction accuracy.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1990년도 한국자동제어학술회의논문집(국내학술편); KOEX, Seoul; 26-27 Oct. 1990
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• pp.30-35
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• 1990

Learning control of a robot manipulator is proposed using the backpropagation neural network. The learning controller is composed of both a linear feedback controller and a neural network-based feedforward controller. The stability analysis of the learning controller is presented. Three energy functions are selected in teaching the neural network controller : 1/2.SIGMA.vertical bar torque error vertical bar $^{2}$, 1/2.SIGMA..alpha. vertical bar position error vertical bar $^{2}$ + .betha. vertical bar velocity error vertical bar $^{2}$ + .gamma. vertical bar acceleration error vertical bar $^{2}$ and learning methods are presented. Simulation results show that the learning controller which is learned to minimize the third energy function performs better than the others in tracking problems. Some properties of the learning controller are discussed with simulation results.

• 로봇학회논문지
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• 제19권1호
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• pp.92-97
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• 2024

This paper provides a study of distributional perspective on reinforcement learning for application in mobile robot navigation. Mapless navigation algorithms based on deep reinforcement learning are proven to promising performance and high applicability. The trial-and-error simulations in virtual environments are encouraged to implement autonomous navigation due to expensive real-life interactions. Nevertheless, applying the deep reinforcement learning model in real tasks is challenging due to dissimilar data collection between virtual simulation and the physical world, leading to high-risk manners and high collision rate. In this paper, we present distributional reinforcement learning architecture for mapless navigation of mobile robot that adapt the uncertainty of environmental change. The experimental results indicate the superior performance of distributional soft actor critic compared to conventional methods.

• 인터넷정보학회논문지
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• 제16권5호
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• pp.99-105
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• 2015

현재 로봇은 교육적 목적으로 학교에서 점차 많이 사용되고 있다. 특히 로봇활용교육의 확산과 더불어, 로봇활용교육의 장점은 학생들에게 창의력과 논리적 사고력을 향상시키는 것으로 알려져 있다. 비록 로봇이 학생들의 수업활동을 위해 매우 유용한 도구임에도 불구하고, 로봇활용교육을 위한 교수-학습모형은 많지 않은 실정이다. 본 논문의 목적은 로봇활용교육을 위한 교수-학습 모형을 개발하는 것이다. 본 논문에서 제안하는 교수-학습 모형은 구성주의 교육철학에 기반을 두어 고안되었고, 6단계(준비, 디자인, 조립, 시범작동, 평가 및 적용 및 확장)로 구성되었다. 본 논문에서 제안하는 모형은 다음과 같은 특징을 지니고 있다. 첫째, 제안하는 모형은 학생들의 창의성과 논리적 사고력을 향상시키기 위해서 디자인되었다. 학생들은 자기주도활동을 해야 하며, 자신의 아이디어에 기초하여 결과물을 제작해야 한다. 교사들은 필요한 경우 학생들을 중재해야 한다. 둘째, 학생들은 본 모형을 통해서 다양한 상호작용을 통해 학습에 참여할 수 있다. 본 모형에서 제공하는 상호작용은 학생-학생, 학생-교사 및 학생-전문가 상호작용을 제공한다. 본 모형은 협력학습을 통한 문제해결을 권장한다. 교사는 필요한 경우 학생들을 안내하고 학생들의 활동을 모두 주시해야 한다. 셋째, 제안 모형은 학생들에게 동기부여를 학습 활동초기에 제공한다. 마지막으로 본 모형에서는 학습 결과뿐만 아니라 학습 과정까지 투명하게 볼 수 있어 학생들의 수업단계도 쉽게 확인할 수 있다. 또한, 학습과정은 최종단계에서 검증할 수 있다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권3호
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• pp.754-774
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• 2023

Wrestling is one of the popular events for modern sports. It is difficult to quantitatively describe a wrestling game between athletes. And deep learning can help wrestling training by human recognition techniques. Based on the characteristics of latest wrestling competition rules and human recognition technologies, a set of wrestling competition video analysis and retrieval system is proposed. This system uses a combination of literature method, observation method, interview method and mathematical statistics to conduct statistics, analysis, research and discussion on the application of technology. Combined the system application in targeted movement technology. A deep learning-based facial recognition psychological feature analysis method for the training and competition of classical wrestling after the implementation of the new rules is proposed. The experimental results of this paper showed that the proportion of natural emotions of male and female wrestlers was about 50%, indicating that the wrestler's mentality was relatively stable before the intense physical confrontation, and the test of the system also proved the stability of the system.

• 한국생산제조학회지
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• 제6권1호
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• pp.7-17
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• 1997

During the past decade, there were many well-established theories for the adaptive control of linear systems, but there exists relatively little general theory for the adaptive control of nonlinear systems. Adaptive control technique is essential for providing a stable and robust performance for application of industrial robot control. Neural network computing methods provide one approach to the development of adaptive and learning behavior in robotic system for manufacturing. Computational neural networks have been demonstrated which exhibit capabilities for supervised learning, matching, and generalization for problems on an experimental scale. Supervised learning could improve the efficiency of training and development of robotic systems. In this paper, a new scheme of adaptive-neuro control system to implement real-time control of robot manipulator using digital signal processors is proposed. Digital signal processors, DSPs, are micro-processors that are developed particularly for fast numerical computations involving sums and products of variables. The proposed neuro control algorithm is one of learning a model based error back-propagation scheme using Lyapunov stability analysis method. The proposed adaptive-neuro control scheme is illustrated to be an efficient control scheme for implementation of real-time control for SCARA robot with four-axes by experiment.

• 제어로봇시스템학회논문지
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• 제12권4호
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• pp.315-319
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• 2006

Support vector machines in biped humanoid robot are presented in this paper. The trajectory of the ZMP in biped walking robot poses an important criterion for the balance of the walking robots but complex dynamics involved make robot control difficult. We are establishing empirical relationships based on the dynamic stability of motion using SVMs. SVMs and kernel method have become very popular method for learning from examples. We applied SVM to model the practical humanoid robot. Three kinds of kernels are employed also and each result has been compared. As a result, SVM based on kernel method have been found to work well. Especially SVM with RBF kernel function provides the best results. The simulation results show that the generated ZMP from the SVM can be improve the stability of the biped walking robot and it can be effectively used to model and control practical biped walking robot.

• 정보교육학회논문지
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• 제11권2호
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• pp.195-203
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• 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.

• 반도체디스플레이기술학회지
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• 제21권4호
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• pp.65-70
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• 2022

In manufacturing and semiconductor industries, transfer robots increase productivity through accurate and continuous work. Due to the nature of the semiconductor process, there are environments where humans cannot intervene to maintain internal temperature and humidity in a clean room. So, transport robots take responsibility over humans. In such an environment where the manpower of the process is cutting down, the lack of maintenance and management technology of the machine may adversely affect the production, and that's why it is necessary to develop a technology for the machine failure diagnosis system. Therefore, this paper tries to identify various causes of failure of transport robots that are widely used in semiconductor automation, and the Prognostics and Health Management (PHM) method is considered for determining and predicting the process of failures. The robot mainly fails in the driving unit due to long-term repetitive motion, and the core components of the driving unit are motors and gear reducer. A simulation drive unit was manufactured and tested around this component and then applied to 6-axis vertical multi-joint robots used in actual industrial sites. Vibration data was collected for each cause of failure of the robot, and then the collected data was processed through signal processing and frequency analysis. The processed data can determine the fault of the robot by utilizing machine learning algorithms such as SVM (Support Vector Machine) and KNN (K-Nearest Neighbor). As a result, the PHM environment was built based on machine learning algorithms using SVM and KNN, confirming that failure prediction was partially possible.