• 한국공작기계학회:학술대회논문집
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• 한국공작기계학회 1999년도 추계학술대회 논문집 - 한국공작기계학회
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• pp.113-118
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• 1999

This paper presents a new approach to the design of neural control system using digital signal processors in order to improve the precision and robustness. Robotic manipulators have become increasingly important n the field of flexible automation. High speed and high-precision trajectory tracking are indispensable capabilities for their versatile application. The need to meet demanding control requirement in increasingly complex dynamical control systems under significant uncertainties, leads toward design of intelligent manipulation robots. The TMS320C31 is used in implementing real time neural control to provide an enhanced motion control for robotic manipulators. In this control scheme, the networks introduced are neural nets with dynamic neurons, whose dynamics are distributed over all the network nodes. The nets are trained by the distributed dynamic back propagation algorithm. The proposed neural network control scheme is simple in structure, fast in computation, and suitable for implementation of real-time control. Performance of the neural controller is illustrated by simulation and experimental results for a SCARA robot.

• 한국지능시스템학회:학술대회논문집
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• 한국지능시스템학회 2008년도 춘계학술대회 학술발표회 논문집
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• pp.111-114
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• 2008

보안이 중요시 되는 공간에서 지능형 감시 시스템의 필요성이 점차 중요시 되고 있다. 본 논문에서는 embedded Linux 기반의 Mobile Robot에 Network Camera를 탑재 하여 침입자를 추적할 수 있는 시스템 구현에 목적을 두고 있다. Network Camera부터 Wireless Lan을 이용하여 서버로 영상을 전송하고, 서버에서 블록매칭 알고리즘을 이용하여 침입자의 이동경로를 파악하며 침입자에 대한 방향 정보를 전송하여 침입자를 추적한다. 로봇이 침입자를 추적함에 따라 침입자의 유효 영상을 얻는다. 본 논문에 의해서 구현된 시스템은 다른 감시 시스템과 연동하여 지능형 감시 시스템으로서 신뢰성을 더할 수 있다.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제7권2호
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• pp.96-101
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• 2007

Neural networks are known as kinds of intelligent strategies since they have learning capability. There are various their applications from intelligent control fields; however, their applications have limits from the point that the stability of the intelligent control systems is not usually guaranteed. In this paper we propose an adaptive tracking control for robot manipulators using the radial basis function network (RBFN) that is e. kind of neural networks. Adaptation laws for parameters of the RBFN are developed based on the Lyapunov stability theory to guarantee the stability of the overall control scheme. Filtered tracking errors between actual outputs and desired outputs are discussed in the sense of the uniformly ultimately boundedness(UUB). Additionally, it is also shown that parameters of the RBFN are bounded. Experimental results for a SCARA-type robot manipulator show that the proposed adaptive tracking controller is adaptable to the environment changes and is more robust than the conventional PID controller and the neuro-controller based on the multilayer perceptron.

• 전자공학회논문지S
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• 제35S권3호
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• pp.102-109
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• 1998

In this paper, we propose a method of cooperative control based on immune system in distributed autonomous robotic system(DARS). Immune system is living body's self-protection and self-maintenance system. Thus these features can be applied to decision making of optimal swarm behavior in dynamically changing environment.For the purpose of applying immune system to DARS, a robot is regarded as a B lymphocyte(B cell), each environmental condition as an antigen, and a behavior strategy as an antibody respectively. The executing process of proposed method is as follows. When the environmental codintion changes, a robot select an appropriate beharior stategy. And its behavior stategy is stimulated and suppressed by other robot using communiation. Finally much stimulated strategy is adopted as a swarm behavior strategy. This control scheme is based on clonal selection and idotopic network hypothesis. And it is used for decision making of optimal swarm stragegy.

• 한국정보통신학회논문지
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• 제5권4호
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• pp.756-765
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• 2001

An effective dexterous motion control method of redundant robot manipulators based on neural optimization network is proposed to satisfy multi-criteria such as singularity avoidance, minimizing energy consumption, and avoiding physical limits of actuator, while performing a given task. The method employs a neural optimization network with parallel processing capability, where only a simple geometric analysis for resolved motion of each joint is required instead of computing of the Jacobian and its pseudo inverse matrix. For dexterous motion, a joint geometric manipulability measure(JGMM) is proposed. JGMM evaluates a contribution of each joint differential motion in enlarging the length of the shortest axis among principal axes of the manipulability ellipsoid volume approximately obtained by a geometric analysis. Redundant robot manipulators is then controlled by neural optimization networks in such a way that 1) linear combination of the resolved motion by each joint differential motion should be equal to the desired velocity, 2) physical limits of joints are not violated, and 3) weighted sum of the square of each differential joint motion is minimized where weightings are adjusted by JGMM. To show the validity of the proposed method, several numerical examples are illustrated.

• 로봇학회논문지
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• 제18권2호
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• pp.143-154
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• 2023

In this study, a fast motion planning method for the swing motion of a 6x6 wheel-legged robot to traverse large obstacles and gaps is proposed. The motion planning method presented in the previous paper, which was based on trajectory optimization, took up to tens of seconds and was limited to two-dimensional, structured vertical obstacles and trenches. A deep neural network based on one-dimensional Convolutional Neural Network (CNN) is introduced to generate keyframes, which are then used to represent smooth reference commands for the six leg angles along the robot's path. The network is initially trained using the behavioral cloning method with a dataset gathered from previous simulation results of the trajectory optimization. Its performance is then improved through reinforcement learning, using a one-step REINFORCE algorithm. The trained model has increased the speed of motion planning by up to 820 times and improved the success rates of obstacle crossing under harsh conditions, such as low friction and high roughness.

• 한국콘텐츠학회:학술대회논문집
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• 한국콘텐츠학회 2003년도 추계종합학술대회 논문집
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• pp.447-453
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• 2003

Emotion을 이용한 컴퓨터 인공 지능, 그래픽, 로봇, 상호 작용 등 다양한 분야에 나타나고 있다. 이에 각 분야에 적용되어진 이론적 배경과, 적용의 특징, 기술 등을 본 글에서 다루고자 한다. 먼저 이론적 접근방식에 있어서는 심리학적 접근과, 사람의 감정 연구, Behavior-Bas설 접근, 생물 행동적 접근, 등이 있으며 이를 구현하기 위한 기술로는 학습 알고리즘, Neural Network 의 Self-Organizing Maps, Fuzzy Cognition Maps등이 있다. 적용 분야로는 Software Agent, Agent Robot과 Entrainment Robot 등이 있다. 본 글에서는 이들의 적용 사례들을 살펴보고 Emotion Architecture에 대해서 분석하고자 한다.

• 제어로봇시스템학회논문지
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• 제12권9호
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• pp.868-875
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• 2006

The localization is one of the most important issues for mobile robot. This paper describes a novel localization system for the development of a location sensing network. The system comprises wirelessly controlled infrared landmarks and an image sensor which detects the pixel positions of infrared sources. The proposed localization system can operate irrespective of the illumination condition in the indoor environment. We describe the operating principles of the developed localization system and report the performance for mobile robot localization and navigation. The advantage of the developed system lies in its robustness and low cost to obtain location information as well as simplicity of deployment to build a robot location sensing network. Experimental results show that the developed system outperforms the state-of-the-art localization methods.

• 한국지능시스템학회논문지
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• 제18권2호
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• pp.163-168
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• 2008

이동로봇의 위치인식과 이를 바탕으로 하는 주행시스템은 이동로봇 핵심기술 중의 하나이다. 무선 센서 네트워크는 저전력, 저가, 단순성 등이 주된 특징으로서 실내 위치인식 시스템의 응용에 있어서 많은 가능성을 지니고 있다. 본 논문에서는 ZigBee 기반 무선 센서 네트워크에 퍼지 모델링 방법을 사용하여 이동로봇의 실내 위치인식 알고리즘을 구현하여 이를 주행시스템에 적용하고자 한다. ZigBee 기반 센서 네트워크에서는 노드들 간의 거리를 인식하기 위해서 RSSI (Received Signal Strength Indication) 값을 이용하게 된다. 그러나 이 RSSI 값은 건물 주위 물체에 의해 왜곡되거나 반사되는 수신 신호의 특성에 의해 영향을 받게 된다. 따라서 정확한 거리 정보를 알아내기 위해서는 적절한 교정 방법이 필요하며, RSSI 값에 퍼지 모델링 기법을 이용하여 정확한 거리 정보를 추출하고자 한다. 또한 이 거리 정보를 바탕으로 동적 삼각측량법을 이용하여 이동로봇의 실내 위치를 효율적으로 인식하고 주변 상항 변화에 효과적으로 대처할 수 있는 주행 알고리즘을 개발하고자 한다.

• 인터넷정보학회논문지
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• 제5권6호
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• pp.1-10
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• 2004

A web robot is a software that has abilities of tracking and collecting web documents on the Internet(l), The performance scalability of recent web robots reached the limit CIS the number of web documents on the internet has increased sharply as the rapid growth of the Internet continues, Accordingly, it is strongly demanded to study on the performance scalability in searching and collecting documents on the web. 'Design of web robot based on Multi-Agent to speed up documents collection ' rather than 'Sequentially executing Web Robot based on the existing Fork-Join method' and the results of analysis on its performance scalability is presented in the thesis, For collection speedup, a Multi-Agent based web robot performs the independent process for inactive URL ('Dead-links' URL), which is caused by overloaded web documents, temporary network or web-server disturbance, after dividing them into each agent. The agents consist of four component; Loader, Extractor, Active URL Scanner and inactive URL Scanner. The thesis models a Multi-Agent based web robot based on 'Amdahl's Law' to speed up documents collection, introduces a numerical formula for collection speedup, and verifies its performance improvement by comparing data from the formula with data from experiments based on the formula. Moreover, 'Dynamic URL Partition algorithm' is introduced and realized to minimize the workload of the web server by maximizing a interval of the web server which can be a collection target.