• The Journal of Korea Robotics Society
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• v.5 no.1
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• pp.64-69
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• 2010

In this paper, we propose remote navigation control for intelligent robot using particle swarm optimization(PSO). The proposed system consists of interfaces for intelligent robot navigation and user interface in order to control the intelligent robot remotely. And communication interfaces using TCP/IP socket is used. To do this, we first design the fuzzy navigation controller based on expert's knowledge for intelligent robot navigation. At this time, we use the PSO algorithm in order to identify the membership functions of fuzzy control rules. And then, we propose the remote system in order to navigate the robot remotely. Finally, we show the effectiveness and feasibility of the developed controller and remote system through some experiments.

• Journal of IKEEE
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• v.20 no.2
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• pp.196-199
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• 2016

As the children with Autistic Spectrum Disorders(ASD) are increasing, and study of intelligent robot is dynamically proceeding, therapeutic robots are needed to help ASD children who have a lack of social interaction, inadequacy of communication. This paper deals with the design and operation of a cat robot for the treatment. To configure the behavior based on the results of field experiments, emotional treatment is suggested and robot structure considering ASD characteristic is designed with some sensors. In addition, we verified the effectiveness of the operation by field test.

• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.5
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• pp.660-666
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• 2011

In this paper, we proposed a Particle Swarm Optimization(PSO) to search the optimal link lengths for legged walking robot. In order to apply the PSO algorithm for the proposed, its walking robot kinematic analysis is needed. A crab robot based on four-bar linkage mechanism and Jansen mechanism is implemented in H/W. For the performance index of PSO, the stride length of the legged walking robot is defined, based on the propose kinematic analysis. Comparative simulation results present to illustrate the viability and effectiveness of the proposed method.

• Journal of Mechanical Science and Technology
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• v.18 no.8
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• pp.1288-1296
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• 2004

Wall-following control problem for a mobile robot is to move it along a wall at a constant speed and keep a specified distance to the wall. This paper proposes wall-following controllers based on Lyapunov function candidate for a two-wheeled mobile robot (MR) to follow an unknown wall. The mobile robot is considered in terms of kinematic model in Cartesian coordinate system. Two wall-following feedback controllers are designed: full state feedback controller and observer-based controller. To design the former controller, the errors of distance and orientation of the mobile robot to the wall are defined, and the feedback controller based on Lyapunov function candidate is designed to guarantee that the errors converge to zero asymptotically. The latter controller is designed based on Busawon's observer as only the distance error is measured. Additionally, the simulation and experimental results are included to illustrate the effectiveness of the proposed controllers.

• Proceedings of the KIEE Conference
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• 2003.11b
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• pp.247-250
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• 2003

In this paper, we propose a robust controller for motion control of n-link robot manipulators using visual feedback. The desired joint velocity and acceleration is obtained by the feature-based visual systems and is used in the joint velocity control loop for trajectory control of the robot manipulator. We design a robust controller that compensates for bounded parametric uncertainties of robot dynamics. The stability analysis of robust joint velocity control system is shown by Lyapunov Method. The effectiveness of the proposed method is shown by simulation results on the 5-link robot manipulators with two degree of freedom.

• Proceedings of the KIEE Conference
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• 2006.07d
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• pp.1860-1861
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• 2006

In this paper, we design a robust controller of biped robot system with uncertainties, using recurrent neural network. In our proposed control system, we use the self-recurrent wavelet neural network (SRWNN). The SRWNN makes up for the weak points in wavelet neural network(WNN). While the WNN has fast convergence ability, it dose not have a memory. So the WNN cannot confront unexpected change of the system. However, the SRWNN, having advantage of WNN such as fast convergence, can easily encounter the unexpected change of the system. For stable walking control of biped robot, we use sliding mode control (SMC). Here, uncertainties are predicted by SRWNN. The weights of SRWNN are trained by adaptive laws based on Lyapunov stability theorem. Finally, we carry out computer simulations with a biped robot model to verify the effectiveness of the proposed control system,.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.2102-2107
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• 2005

In this paper, a real-time optimization method for nonlinear dynamical systems is proposed. The proposed method is based on the algorithms of numerical solutions for optimal control problems. We deal with a real-time collision-free motion control of a nonholonomic mobile robot, which has input restrictions of actuators. The effectiveness of the algorithmic method is demonstrated through numerical and experimental results. The mobile robot which we have developed is able to avoid moving obstacles skillfully. Therefore the proposed controller works well in real time.

• Journal of the Institute of Convergence Signal Processing
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• v.3 no.2
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• pp.76-82
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• 2002

The objective of this paper is to design a robust controller for a flexible robot arm manipulator using LMI(Linear Matrix Inequality) theory, and confirm its effectiveness through experimentation. We first describe a modeling Process of the flexible arm in order to get a mathematical model, and then discuss how to approximately obtain the uncertainty of the model for robust control. As to the control system design, we adopt the LMI-based H$_{\infty}$ synthesis algorithm which has the merits of eliminating the regularity restrictions attached to the Riccati-based methods. As a result of this, we can cope with the parameter variation (that is, modeling uncertainty) due to the tip-load variation. Finally we confirm the effectiveness of the controller through experiment and simulation.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.22 no.3_1spc
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• pp.622-628
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• 2013

This paper describes the development of a thigh-muscle strength-assistance robot, which is a kind of wearable robot. For practicality and commercialization, we proposed three fundamental concepts: the reduction of the thigh-muscle strength, minimized degree of dependence on a powered actuator, and complete wearer safety. Based on these concepts, a spring and link bar mechanism was conceived as a novel idea. The movement of the thigh is transferred to the spring mechanism through the link bar; hence, the elastic force of the spring assists the thigh muscle. Using forse sensing resistor (FSR) sensors and a powered cam mechanism, the muscle assistance is automatically activated and deactivated according to the wearer's movement. The specific mechanisms of the robot are addressed in detail, and the effectiveness is verified by experiments.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.191-196
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• 2003

This paper presents a method to design an adaptive controller for the kinematic model of a two-wheeled welding mobile robot (WMR) with unknown parameters. We propose a nonlinear controller based on the Lyapunov function to enhance the tracking properties of the WMR. The WMR can track any smooth curved welding path at a constant velocity of the welding point. The system has three degrees of freedom including two wheels and one torch slider. Torch slider motion is used for fast tracking. To design the tracking performance, the errors from WMR to steel wall is defined, and the controller is designed to drive the errors to zero as fast as possible. The effectiveness of the proposed controller is shown through simulation results.