• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.2319-2324
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• 2005

This paper proposes a intelligent gain and boundary layer based sliding mode control (SMC) method for robotic systems with unknown model uncertainties. For intelligent gain and boundary layer, we employ the self recurrent wavelet neural network (SRWNN) which has the properties such as a simple structure and fast convergence. In our control structure, the SRWNNs are used for estimating the width of boundary layer, uncertainty bound, and nonlinear terms of robotic systems. The adaptation laws for all parameters of SRWNNs and reconstruction error bounds are derived from the Lyapunov stability theorem, which are used for an on-line control of robotic systems with unknown uncertainties. Accordingly, the proposed method can overcome the chattering phenomena in the control effort and has the robustness regardless of unknown uncertainties. Finally, simulation results for the three-link manipulator, one of the robotic systems, are included to illustrate the effectiveness of the proposed method.

• Proceedings of the KIEE Conference
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• 2005.07d
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• pp.2711-2713
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• 2005

This paper presents the tracking control method of robotic systems with uncertainties using self recurrent wavelet neural network (SRWNN) via the backstepping design technique. The SRWNN is used as the uncertainty observer of the robotic systems. The adaptation laws for weights of the robotic systems are induced from the Lyapunov stability theorem, which are used for on-line controlling robotic systems. Computer simulations of a three-link robot manipulator with uncertainties verify the validity of the proposed SRWNN controller.

• Journal of Institute of Control, Robotics and Systems
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• v.14 no.10
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• pp.1014-1021
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• 2008

This paper proposes an intelligent sliding mode control method for robotic systems with the unknown bound of model uncertainties. In our control structure, the unknown bound of model uncertainties is used as the gain of the sliding controller. Then, we employ the function approximation technique to estimate the unknown nonlinear function including the width of boundary layer and the uncertainty bound of robotic systems. The adaptation laws for all parameters of the self-recurrent wavelet neural network and those for the reconstruction error compensator are derived from the Lyapunov stability theorem, which are used for an on-line control of robotic systems with model uncertainties and external disturbances. Accordingly, the proposed method can not only overcome the chattering phenomenon in the control effort but also have the robustness regardless of model uncertainties and external disturbances. Finally, simulation results for the five-link biped robot are included to illustrate the effectiveness of the proposed method.

• 제어로봇시스템학회:학술대회논문집
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• 1989.10a
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• pp.1085-1090
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• 1989

In this paper, an adaptive control method is presented to guarantee the ultimate boundedness of uncertain systems with partially known uncertainty bounds. This method, with a conventional linear compensator, is used to improve the performance of the trajectory tracking of a robotic manipulator with uncertainties. The proposed method is simulated under several different environments, and its performance is compared with the computed torque method. The simulation results show that the proposed method is well suited for high-performance operation of uncertain robotic systems.

• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.78.4-78
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• 2002

$\textbullet$ Introduction $\textbullet$ System hardware base $\textbullet$ System software implementation $\textbullet$ Connect robotic systems to Internet $\textbullet$ Conclusions

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.2719-2724
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• 2003

In this paper, we deal with the kinematic and dynamic modeling of hybrid robotic systems that are constructed by combination of parallel and serial modules or series of parallel modules. Previously, open-tree structure has been employed for dynamic modeling of hybrid robotic systems. Though this method is generally used, however, it requires expensive computation as the size of the system increases. Therefore, we propose an efficient dynamic modeling methodology for hybrid robotic systems. Initially, the dynamic model for the proximal module is obtained with respect to the independent joint coordinates. Then, in order to represent the operational dynamics of the proximal module, we model virtual joints attached at the top platform of the proximal module. The dynamic motion of the next module exerts dynamic forces to the virtual joints, which in fact is equivalent to the reaction forces exerted on the platform of the lower module by the dynamics of the upper module. Then, the dynamic forces at the virtual joints are distributed to the independent joints of the proximal module. For multiple modules, this scheme can be constructed as a recursive dynamic formulation, which results in reduction of the complexness of the open-tree structure method for modeling of hybrid robotic systems. Simulation for inverse dynamics is performed to validate the proposed modeling algorithm.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.5
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• pp.635-641
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• 2004

A human-friendly interactive system that is based on the harmonious symbiotic coexistence of human and robots is explored. Based on interactive technology paradigm, a robotic cane is proposed for blind or visually impaired travelers to navigate safely and quickly through obstacles and other hazards faced by blind pedestrians. Robotic aids, such as robotic canes, require cooperation between human and robots. Various methods for implementing the appropriate cooperative recognition, planning, and acting, have been investigated. The issues discussed include the interaction of human and robot, design issues of an interactive robotic cane, and behavior arbitration methodologies for navigation planning.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.39 no.9
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• pp.944-955
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• 1990

A high-performance robotic controller is proposed for uncertain robots by using an adaptive control method, which guarantees the boundedness of uncertain systems with partially known uncertainty bounds. In order to improve the tracking performance of the robotic controller, a linear compensator is introduced to the robotic system which has been linearized via a nonlinear feedback. The above adaptive method is then utilized to guarantee the ultimate boundedness of the tracking errors. The performance of the robotic controller is compared with that of the computed torque method by computer simulations under uncertain environments. The simulation results show that the proposed method gives better performance than the computed torque method. Since the proposed method has a small number of parameters to be estimated, the controller is simpler to implement than other existing adaptive controller for robots. Hence, the proposed robotic control method is expected to be well suited for high-performance operation of robots under uncertain environment.

• ETRI Journal
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• v.27 no.6
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• pp.666-676
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• 2005

In recent years, motivated by the emergence of ubiquitous computing technologies, a new class of networked robots, ubiquitous robots, has been introduced. The Ubiquitous Robotic Companion (URC) is our conceptual vision of ubiquitous service robots that provide users with the services they need, anytime and anywhere in ubiquitous computing environments. To realize the vision of URC, one of the essential requirements for robotic systems is to support ubiquity of services: that is, a robot service must be always available even though there are changes in the service environments. Specifically robotic systems need to be automatically interoperable with sensors and devices in current service environments, rather than statically preprogrammed for them. In this paper, the design and implementation of a semantic-based ubiquitous robotic space (SemanticURS) is presented. SemanticURS enables automated integration of networked robots into ubiquitous computing environments exploiting Semantic Web Services and AI-based planning technologies.

• The Journal of Korea Robotics Society
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• v.17 no.3
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• pp.282-287
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• 2022

High shear adhesion on wet and rough surfaces and tactile feedback of gripping forces are highly important for realizing robotic gripper systems. Here, we propose a bioinspired robotic gripper with highly shear adhesion and sensitive pressure sensor for tactile feedback systems. To achieve them, we fabricated multi-walled carbon nanotube sensing layer on a thin polymeric adhesive layer of polydimethylsiloxane. With densely hexagonal-packed microstructures, the pressure sensor achieved 9 times the sensing property of a sensor without microstructures. We then assembled hexagonal microstructures inspired by the toe pads of a tree frog, giving strong shear adhesion under both dry and wet surfaces such as silicon (42 kPa for dry and ~30 kPa for underwater conditions) without chemical-residues after detachment. Our robotic gripper can prevent damage to weak or smooth surfaces that can be damaged at low pressure through pressure signal feedback suggesting a variety of robotic applications.