• 한국정밀공학회:학술대회논문집
• /
• 한국정밀공학회 2001년도 춘계학술대회 논문집
• /
• pp.249-254
• /
• 2001

Abstract In this paper, we propose a method of cooperative control based on artifical intelligent system in distributed autonomous robotic system. In general, multi-agent behavior algorithm is simple and effective for small number of robots. And multi-robot behavior control is a simple reactive navigation strategy by combining repulsion from obstacles with attraction to a goal. However when the number of robot goes on increasing, this becomes difficult to be realized because multi-robot behavior algorithm provide on multiple constraints and goals in mobile robot navigation problems. As the solution of above problem, we propose an architecture of fuzzy system for each multi-robot speed control and fuzzy-neural network for obstacle avoidance. Here, we propose an architecture of fuzzy system for each multi-robot speed control and fuzzy-neural network for their direction to avoid obstacle. Our focus is on system of cooperative autonomous robots in environment with obstacle. For simulation, we divide experiment into two method. One method is motor schema-based formation control in previous and the other method is proposed by this paper. Simulation results are given in an obstacle environment and in an dynamic environment.

• 제어로봇시스템학회논문지
• /
• 제10권9호
• /
• pp.799-808
• /
• 2004

In this paper, a new collision avoidance algorithm based on the dynamic model of a mobile robot is proposed. In order to avoid obstacles on the path of a mobile robot, intelligent force control is used to regulate accurate distance between a robot and an obstacle. Since uncertainties from robot and environment dynamics degrade the performance of a collision avoidance task, neural network is used to compensate for uncertainties so that the collision avoidance can be performed intelligently. Simulation studies are conducted to confirm the proposed collision avoidance tracking control algorithm.

• 대한전기학회:학술대회논문집
• /
• 대한전기학회 2003년도 학술회의 논문집 정보 및 제어부문 A
• /
• pp.255-258
• /
• 2003

It is hard to centrol the wheeled mobile robot because of uncertainty of modeling, non-holonomic constraint and so on. To solve the problems, we design the controller of wheeled mobile robot based on fuzzy-neural network algorithm. In this paper, we should research the problem of classical controller for path-tracking algorithm and design of Fuzzy-Neural Network algorithm controller. Classical controller acquired different control value according to change of initial position and direction. In this control value having very difficult and having acquired a lot of trial and error Fuzzy is implemented to adaptive adjust control value by error and change of error and neural network is implemented to adaptive adjust the control gain during the optimization. The computer simulation shows that the proposed fuzzy-neural network controller is effective.

• 로봇학회논문지
• /
• 제13권4호
• /
• pp.213-219
• /
• 2018

In this paper, we introduce a target position reasoning system based on Bayesian network that selects destinations of robots on a map to explore compound disaster environments. Compound disaster accidents have hazardous conditions because of a low visibility and a high temperature. Before firefighters enter the environment, the robots notify information in advance, such as victim's positions, number of victims, and status of debris of building. The problem of the previous system is that the system requires a target position to operate the robots and the firefighter need to learn how to use the robot. However, selecting the target position is not easy because of the information gap between eyewitness accounts and map coordinates. In addition, learning the technique how to use the robots needs a lot of time and money. The proposed system infers the target area using Bayesian network and selects proper x, y coordinates on the map based on image processing methods of the map. To verify the proposed system, we designed three example scenarios based on eyewetinees testimonies and compared time consumption between human and the system. In addition, we evaluate the system usability by 40 subjects.

• 로봇학회논문지
• /
• 제4권4호
• /
• pp.312-319
• /
• 2009

This paper proposes a low-complexity indoor localization method of mobile robot under the dynamic environment by fusing the landmark image information from an ordinary camera and the distance information from sensor nodes in an indoor environment, which is based on sensor network. Basically, the sensor network provides an effective method for the mobile robot to adapt to environmental changes and guides it across a geographical network area. To enhance the performance of localization, we used an ordinary CCD camera and the artificial landmarks, which are devised for self-localization. Experimental results show that the real-time localization of mobile robot can be achieved with robustness and accurateness using the proposed localization method.

• 제어로봇시스템학회논문지
• /
• 제16권2호
• /
• pp.114-119
• /
• 2010

Network technology has been developed rapidly for digital service in these days. ZigBee, one of the IEEE 802.15.4 protocols, supporting local communication has become the core technology in the wireless network area. In this paper we designed an integrated fire monitoring system using a mobile robot and the ZigBee sensor nodes which are deployed to monitor fires. When a fire breaks out, the image information of the scene of a fire is transmitted by an autonomous mobile robot and we also monitor the current position of the robot. Furthermore, the data around the place where the fire breaks out and the positions of the sensor nodes can be transmitted to a server via the multi-hop communication in the real time.

• 제어로봇시스템학회:학술대회논문집
• /
• 제어로봇시스템학회 1989년도 한국자동제어학술회의논문집; Seoul, Korea; 27-28 Oct. 1989
• /
• pp.1113-1119
• /
• 1989

Neural network control has many innovative potentials for intelligent adaptive control. Among many, it promises real time adaption, robustness, fault tolerance, and self-learning which can be achieved with little or no system models. In this paper, a dynamic robot controller has been developed based on a backpropagation neural network. It gradually learns the robot's dynamic properties through repetitive movements being initially trained with a PD controller. Its control performance has been tested on a simulated PUMA 560 demonstrating fast learning and convergence.

• 제어로봇시스템학회논문지
• /
• 제14권5호
• /
• pp.467-472
• /
• 2008

In this paper, we design, implement and apply network physical layer to 100 BaseFx optical cable interface module based on industrial ethernet protocol which is physical layer of EtherCAT that has ensure its open standard ethernet compatibility which having been provided with real time of control in network of intelligent service robot, can be process numerous data to sensor and motor control system. Through BLDC motor control performance tests, we try to propose suitability as internal network of intelligent service robot and automation system.

• 대한전기학회논문지:시스템및제어부문D
• /
• 제50권11호
• /
• pp.522-531
• /
• 2001

Recently conventional artificial intelligence(AI) approaches have been employed to build action selectors for the autonomous mobile robot(AMR). However, in these approaches, the decision making process to choose an action from multiple competence modules is still an open question. Many researches have been focused on the reactive planning systems such as the biological immune system. In this paper, we attempt to construct an action selector for an AMR based on the artificial immune network and internet. The information from vision sensors is used for antibody. We propose a learning method for artificial immune network using evolutionary algorithm to produce antibody automatically. The internet environment for an AMR action selector shows the usefulness of the proposed learning artificial immune network application.

• 한국지능시스템학회논문지
• /
• 제17권7호
• /
• pp.887-892
• /
• 2007

본 논문에서는 지능형 서비스 로봇의 네트워크에서 제어의 실시간이 보장되면서 많은 데이터를 처리할 수 있는 개방형 표준 이더넷 호환성을 확보한 산업용 이더넷 프로토콜인 EtherCAT을 기반으로 하여 네트워크의 물리 계층을 100BaseFx인 광케이블 인터페이스 모듈을 설계하고 구현하여 센서 및 모터제어 시스템에 적용하고, 테스트를 통해 지능형 서비스 로봇 내부 네트워크로서의 적합성을 제시하고자 한다.