• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.1804-1807
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• 1997

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 learnign architecture. It is proposed a learning controller consisting of two neural networks-fuzzy based on independent reasoning and a connecton 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.

• Proceedings of the KIEE Conference
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• 2004.11c
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• pp.258-260
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• 2004

The Controller Area Network (CAN) is being widely used in real-time control applications such as automobiles, aircraft, and automated factories. Unfortunately, CAN, in its current form, is not able to either share out the system bandwidth among the different devices fairly or to grant an upper bound on the transmission times experienced by the nodes connected to the communication medium as it happens, for instance, in the token-based networks. In this paper, we present An Application Layer Design for Humanoid Robot in the CAN. Besides introducing the new algorithm, this paper also presents some performance figures obtained using a specially developed software simulator and experimentation for composition of CAN which uses JTAG mode of a parallel debugging., while the behavior of the new algorithm is compared with the traditional CAN systems. in order to see how effective they are.

• Proceedings of the KIEE Conference
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• 2005.07d
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• pp.2717-2719
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• 2005

Generally, biped walking is difficult to control because a biped robot is a nonlinear system with various uncertainties. In this paper, we propose a hybrid control system to improve the efficiency of position tracking performance of biped locomotion. In our control system, the wavelet neural network (WNN) based on Sliding mode controller is used as a main controller which estimates a biped robot model, and the compensated controller is proposed to compensate the estimation error. A WNN is utilized to estimate uncertain and nonlinear system parameters, where the weights of WNN are trained by adaptive laws that are induced from the Lyapunov stability theorem. Finally, the effectiveness of the proposed control system is verified through computer simulations.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.98-100
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• 2005

This paper, proposes concept of personal robot middleware core also called streaming layer. Based on openness and portability, the streaming layer is proposed in order to meet requirements of different kinds of applications. The streaming layer architecture provides effective management of data flows and allows integration of different systems with ease regardless software of hardware platform. With extensibility support additional features can be build in without affect to performance. Therefore, heterogeneous network support, real-time communications, embedded boards support can be easily achieved. In order to achieve high performance together with portability into different platforms, the most functions has to be implemented in C language, while critical parts, such as scheduling, priority assignment has to be made using native functions of tested platforms.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1996.04a
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• pp.685-688
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• 1996

In debureing process. Human experts who have long experenc in performing deburring tasks can manipulate tools efficiently and adapt the tool motions according to the state of the process based on their skills. However, human experts have difficulties in describing linguistically their control schemes and strategies because teey don't aware the detailed process in interpreting wensory information and adapting tool motion. Therefore, it is important to develop a mean of understanding the skills and acquiring the control schemes so that the robot can perform the same skillful motion as the human experts. In this paper, an expert's skill model is developed so that it can be transferred effectively from the expertt to the robot. Skills are represented by a mapping which is generated by using a neural network. Expert's skill shows that the robot is able to associate a correct control strategy with process characteristics which is acquired from a vision image in a similar manner to a human expert's

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.52 no.12
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• pp.679-688
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• 2003

In order to navigate the mobile robots safely in unknown environments, many researches have been studied to devise navigational algorithms for the mobile robots. In this paper, we propose a navigational algorithm that consists of an obstacle-avoidance behavior module, a goal-approach behavior module and a radial basis function network(RBFN) supervisor. In the obstacle-avoidance behavior module and goal-approach behavior module, the fuzzy-artificial immune networks are used to select a proper steering angle which makes the autonomous mobile robot(AMR) avoid obstacles and approach the given goal. The RBFN supervisor is employed to combine the obstacle-avoidance behavior and goal-approach behavior for reliable and smooth motion. The outputs of the RBFN are proper combinational weights for the behavior modules and velocity to steer the AMR appropriately. Some simulations and experiments have been conducted to confirm the validity of the proposed navigational algorithm.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2010.05a
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• pp.975-978
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• 2010

This paper presents a Kalman filter based modeling algorithm for autonomous robots. State of the robot systems is measured by using embedded sensors and then carried to a host computer via ubiquitous sensor network (USN). We settle a linear state space motion equation for unknown system dynamics and modify a popular Kalman filter algorithm in deriving suitable parameter estimation mechanism. We conduct real-time experiment to test our proposed modeling algorithm where velocity state of the constructed robot is used as system observation.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.124-127
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• 1997

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 condition changes, a robot selects an appropriate behavior strategy. And its behavior strategy is simulated and suppressed by other robot using communication. Finally much simulated strategy is adopted as a swarm behavior strategy. This control scheme is based on clonal selection and idiotopic network hypothesis. And it is used for decision making of optimal swarm strategy.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.03a
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• pp.127-130
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• 1998

In this paper, we propose a method of cooperative control(T-cell modeling) and selection of group behavior strategy(B-cell modeling) 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 cell, each environmental condition as an antigen, a behavior strategy as an antibody and control parameter as a T-call respectively. The executing process of proposed method is as follows. When the environmental condition changes, a robot selects an appropriate behavior strategy. And its behavior strategy is stimulated and suppressed by other robot using communication. Finally much stimulated strategy is adopted as a swarm behavior strategy. This control scheme is based of clonal selection and idiotopic network hypothesis. And it is used for decision making of optimal swarm strategy. By T-cell modeling, adaptation ability of robot is enhanced in dynamic environments.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.11 no.6
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• pp.207-214
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• 2011

In this paper, using Zigbee-based wireless sensor networks and Lego MindStorms NXT robot, a remote monitoring and navigation system for mobile robot has been developed. Mobile robot can estimate its position using encoder values of its motor, but due to the existing friction and shortage of motor power etc., error occurs. To fix this problem and obtain more accurate position of mobile robot, a ultrasound module on wireless sensor networks has been used in this paper. To overcome disadvantages of ultrasound which include straightforwardness and narrow detection coverage, we rotate moving node attached to mobile robot by $360^{\circ}$ to measure each distance from four fixed nodes. Then location of mobile robot is estimated by triangulation using measured distance values. In addition, images are sent via a network using a USB Web camera to smart phone. On smart phones we can see location of robot, and images around places where robot navigates. And remote monitoring and navigation is possible by just clicking points at the map on smart phones.