• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 1997.04a
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• pp.125-130
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• 1997

In this paper, it is 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 in 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 TMS320C3x 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, andsuitable for implementation of robust control.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2001.10a
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• pp.128-133
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• 2001

This paper presents a new approach to the design of adaptive control system using DSPs(TMS320C50) for robotic manipulators to achieve trajectory tracking angles. Digital signal processors are used in implementing real time adaptive control algorithms to provide motion for robotic manipulators. In the proposed scheme, adaptation laws are derived from the improved second stability analysis based on the indirect adaptive control theory. The proposed control scheme is simple in structure, fast in computation, an suitable for implementation of real-time control. Moreover, this scheme does not require an accurate dynamic modeling, nor values of manipulator parameters and payload. Performance of the adaptive controller is illustrated by experimental results for a SCARA robot.

• The Journal of Korea Robotics Society
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• v.8 no.3
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• pp.197-205
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• 2013

This paper presents a robotic system that provides telepresence to the visually impaired by combining real-time haptic rendering with multi-modal interaction. A virtual-proxy based haptic rendering process using a RGB-D sensor is developed and integrated into a unified framework for control and feedback for the telepresence robot. We discuss the challenging problem of presenting environmental perception to a user with visual impairments and our solution for multi-modal interaction. We also explain the experimental design and protocols, and results with human subjects with and without visual impairments. Discussion on the performance of our system and our future goals are presented toward the end.

• Proceedings of the KIEE Conference
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• 1988.11a
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• pp.441-444
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• 1988

This paper presents a methodology for developing the graphic simulator for a robotic workcell. The Petri Net model is applied to described the system state and the workcell simulation is performed by the event scheduling approach of the model. For the graphic display of simulation process, the information of the workcell resources are stored in hierachically-structured data_babe, and the geometry of the resources is described by robot modelling, component modelling, and world modelling.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10a
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• pp.815-820
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• 1993

Robotic technology has been grown up conspicuously by its versatility. KAERI has been involved in one of facets of robot industry to keep abreast of rapid evolving technologies In robotic field and has launched long-term R&D plan to assure the stable nuclear energy. In this paper, the latest development status of teleoperated robot system has been presented with emphasis the configuration of overall control system with 3 dimensional graphic system that provides operators with tele-presence situation. This robot system under development, composed of master-slave arm with controller and graphic simulator, is operated by a master manipulator to enable an installation and removal operation of nozzle dam system for steam generator. Evaluation and analysis has been carried out to get optimal parameters of robot system.

• Journal of Institute of Control, Robotics and Systems
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• v.16 no.6
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• pp.601-607
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• 2010

For commercial success of emerging service robots, the fault tolerant technology for system reliability and human safety is crucial. Traditionally fault tolerance methods have been implemented in application level. However, from our studies on the common design patterns in fault tolerance, we argue that a framework-based approach provides many benefits in providing reliability for system development. To demonstrate the benefits, we build a framework-based fault tolerant engine for OPRoS (Open Platform for Robotic Services) standards. The fault manager in framework provides a set of fault tolerant measures of detection, isolation, and recovery. The system integrators choose the appropriate fault handling tools by declaring XML configuration descriptors, considering the constraints of components and operating environment. By building a fault tolerant navigation application from the non-faulttolerant components, we demonstrate the usability and benefits of the proposed framework-based approach.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2001.04a
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• pp.255-260
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• 2001

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 in 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.

• Journal of Advanced Marine Engineering and Technology
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• v.30 no.2
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• pp.231-238
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• 2006

The performance of free-breathing polymer electrolyte membrane fuel cell (PEMFC) was studied experimentally and the effect of cathode separator structure on the cell performance was investigated. Cathode separators were used for the cell with $18cm^2$ active area. In channel type, the contact resistance is low, and the nature convection. which is strongly affected by the cross-sectional shape of cathode separator channel, is dominant in a cell performance. The maximum power density with $18cm^2$ active area is $105mW/cm^2$ using the 10mm depth and 2mm width channel. A computational analysis was optimum structure of free-breathing channel type PEMFC for robotic application.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 1996.10a
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• pp.133-137
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• 1996

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 bevome increasingly important in the field of flexible automation. High speed and high-precision trajectory tracking arre indispensable capabilities for their versatile application. the need to meet demanding control requirement in increasingly complex dynamical control systems under sygnificant uncertainties leads toward design of implementing real time neural control to provide an enhanced motion control for robotic manipulators. In this control scheme the ntworks intrduced are neural nets with dynamic neurouns whose dynamics are distributed over all the network nodes. The nets are trained by the distributed dynamic 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 SCAEA robot.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 1999.10a
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• pp.154-161
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• 1999

This paper presents a new approach to the design of adaptive control system using DSPs(TMS320C30) for robotic manipulators to achieve trajectory tracking by the joint angles. Digital signal processors are used in implementing real time adaptive control algorithms to provide an enhanced motion control for robotic manipulators. In the proposed control scheme, adaptation laws are derived from the improved Lyapunov second stability analysis method based on the adaptive model reference control theory. The adaptive controller consists of an adaptive feedforward controller, feedback controller, and PID type time-varying auxillary control elements. The proposed adaptive control scheme is simple in structure, fast in computation, and suitable for implementation of real-time control. Moreover, this scheme does not require an accurate dynamic modeling, nor values of manipulator parameters and payload. Performance of the adaptive controller is illustrated by simulation and experimental results for a SCARA robot.