• Journal of the Korean Institute of Telematics and Electronics T
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• v.36T no.2
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• pp.48-55
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• 1999

To cope actively with the changes of external environments, it is necessary that a robot should have visual feedback control (VFC) using image informations. A VFC system consists of a manipulator and camera(s). For the fixed visual system, when feature value are located at the same line, we have a problem of singular value unable to be controlled by VFC. As a solution, we may define state values of the image Jacobians, then, by making comparisons and evaluations of feature values, select available ones. This method, however, has a demerit increasing numbers of feature values. To solve the problem, moving cameras of VFC system actively, we suggest an algorithm which dose not cause singular value, and prove its availability through simulations.

• Journal of Institute of Control, Robotics and Systems
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• v.20 no.6
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• pp.606-611
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• 2014

The understanding of another's behavior is a fundamental cognitive ability for primates including humans. Recent neuro-physiological studies suggested that there is a direct matching algorithm from visual observation onto an individual's own motor repertories for interpreting cognitive ability. The mirror neurons are known as core regions and are handled as a functionality of intent recognition on the basis of imitative learning of an observed action which is acquired from visual-information of a goal-directed action. In this paper, we addressed previous works used to model the function and mechanisms of mirror neurons and proposed a computational model of a mirror neuron system which can be used in human-robot interaction environments. The major focus of the computation model is the reproduction of an individual's motor repertory with different embodiments. The model's aim is the design of a continuous process which combines sensory evidence, prior task knowledge and a goal-directed matching of action observation and execution. We also propose a biologically inspired plausible equation model.

• Journal of Institute of Control, Robotics and Systems
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• v.3 no.3
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• pp.297-304
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• 1997

In stereo vision, camera modeling is very important because the accuracy of the three dimensional locations depends considerably on it. In the existing stereo camera models, two camera planes are located in the same plane or on the optical axis. These camera models cannot be used in the active vision system where it is necessary to obtain two stereo images simultaneously. In this paper, we propose four kinds of stereo camera models for active stereo vision system where focal lengths of the two cameras are different and each camera is able to rotate independently. A single closed form solution is obtained for all models. The influence of the stereo camera model to the field of view, occlusion, and search area used for matching is shown in this paper. And errors due to inaccurate focal length are analyzed and simulation results are shown. It is expected that the three dimensional locations of objects are determined in real time by applying proposed stereo camera models to the active stereo vision system, such as a mobile robot.

• The KIPS Transactions:PartA
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• v.16A no.4
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• pp.255-262
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• 2009

We present an autonomous entertainment dolphin robot system based on ubiquitous sensor networks(USN). Generally, It is impossible to apply to USN and GPS in underwater bio-mimetic robots. But An Entertainment dolphin robot which presented in this paper operates on the water not underwater. Navigation of the underwater robot in a given area is based on GPS data and the acquired position information from deployed USN motes with emphasis on user interaction. Body structures, sensors and actuators, governing microcontroller boards, and swimming and interaction features are described for a typical entertainment dolphin robot. Actions of mouth-opening, tail splash or water blow through a spout hole are typical responses of interaction when touch sensors on the body detect users' demand. Dolphin robots should turn towards people who demand to interact with them, while swimming autonomously. The functions that are relevant to human-robot interaction as well as robot movement such as path control, obstacle detection and avoidance are managed by microcontrollers on the robot for autonomy. Distance errors are calibrated periodically by the known position data of the deployed USN motes.

• 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 Korean Institute of Intelligent Systems Conference
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• 2003.09a
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• pp.471-475
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• 2003

This paper proposes a sensor-fusion technique where the data sets for the previous moments are properly transformed and fused into the current data sets to enable accurate measurement, such as, distance to an obstacle and location of the service robot itself. In the conventional fusion schemes, the measurement is dependent on the current data sets. As the results, more of sensors are required to measure a certain physical parameter or to improve the accuracy of the measurement. However, in this approach, instead of adding more sensors to the system, the temporal sequence of the data sets are stored and utilized for the measurement improvement. Theoretical basis is illustrated by examples and the effectiveness is proved through the simulations. Finally, the new space and time sensor fusion (STSF) scheme is applied to the control of a mobile robot in an unstructured environment as well as structured environment.

• The Journal of Korea Robotics Society
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• v.9 no.4
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• pp.258-263
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• 2014

An adaptive robot hand (AR-Hand) has a stable grasp of different objects in unstructured environments. In this study, we propose an AR-Hand based on a tendon-driven mechanism which consists of 4 fingers and 12 DOFs. It weighs 0.5 kg and can grasp an object up to 1 kg. This hand based on the adaptive grasp mechanism is able to provide a stable grasp without a complex control algorithm or sensor system. The fingers are driven by simple tendon structures with each finger capable of adaptively grasping the objects. This paper presents a method to decide the joint stiffness. The adaptive grasping is verified by various grasping experiments involving objects with different shapes and sizes.

• Proceedings of the KIEE Conference
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• 2006.10c
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• pp.530-532
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• 2006

In this paper, we propose stable walking algorithm using ZMP for the biped robot in the slope-way. At first, we define discrete state variables that classified stable area and unstable area by center of mass from ZMP during slope-way walking. For the stable walking gait, the discrete state controller for determining the high-level and low-level decision making are designed. The high-level decision making is composed of the discrete state variables; left foot support phase, right foot support phase, flat-way, and slope-way. Then the continuous state controller is implemented for the low-level decision making using ZMP.

• 제어로봇시스템학회:학술대회논문집
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• 1990.10b
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• pp.1082-1086
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• 1990

This paper gives an account of teleoperated mobile robot system which is intended to operate in hostile environments where human access is limited or prohibited. A prototype mobile robot equipped with manipulator was designed and initial tests were made in laboratory environment. Test results, yet preliminary, have been encouraging for further research efforts. Future plans emerging from these initial results are also summarized.

• Proceedings of the KIEE Conference
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• 1990.07a
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• pp.485-491
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• 1990

We propose a minimum-time path planning soheme for the robot manipulator using Hopfield neural network. The minimum-time path planning, which can allow the robot system to perform the demanded tasks with a minimum execution time, may be of consequence to improve the productivity. But most of the methods proposed till now suffers from a significant computational burden and thus limits the on-line application. One way to avoid such a difficulty is to apply the neural network technique, which can allow the parallel computation, to the minimum-time problem. This paper propose an approach for solving the minimum-time path planning by using Hopfield neural network. The effectiveness of the proposed method is demonstrarted using the PUMA 560 manipulator.