• International Journal of Control, Automation, and Systems
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• v.3 no.4
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• pp.552-563
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• 2005

In this paper, we propose a wavelet based fuzzy neural network (WFNN) based direct adaptive control scheme for the solution of the tracking problem of mobile robots. To design a controller, we present a WFNN structure that merges the advantages of the neural network, fuzzy model and wavelet transform. The basic idea of our WFNN structure is to realize the process of fuzzy reasoning of the wavelet fuzzy system by the structure of a neural network and to make the parameters of fuzzy reasoning be expressed by the connection weights of a neural network. In our control system, the control signals are directly obtained to minimize the difference between the reference track and the pose of a mobile robot via the gradient descent (GD) method. In addition, an approach that uses adaptive learning rates for training of the WFNN controller is driven via a Lyapunov stability analysis to guarantee fast convergence, that is, learning rates are adaptively determined to rapidly minimize the state errors of a mobile robot. Finally, to evaluate the performance of the proposed direct adaptive control system using the WFNN controller, we compare the control results of the WFNN controller with those of the FNN, the WNN and the WFM controllers.

• The Journal of the Korea Contents Association
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• v.5 no.5
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• pp.85-91
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• 2005

Recently, home network system is providing more various services as home robot applied. A home robot not only basically controls home device but also services prevention of crimes, prevention of disasters through home monitoring and various entertainments while it navigates the autonomously based home network system. However, for the existing home robot to it is not easy to install all functions because the size of robot device becomes larger and the management of contents and applications executed becomes uneasy and has difficulties in adding new functions. Moreover many improvements are necessary for functioning of robot's location awareness. In this paper, we propose a more improved home robot system which uses resources of the robot efficiently as it divides the complicated operation of the robot among external digital device and adds new functions easily and recognizes the location of the robot by RFID.

• The Journal of Korea Robotics Society
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• v.14 no.2
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• pp.94-103
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• 2019

The optimal grasping point of the object varies depending on the shape of the object, such as the weight, the material, the grasping contact with the robot hand, and the grasping force. In order to derive the optimal grasping points for each object by a three fingered robot hand, optimal point and posture have been derived based on the geometry of the object and the hand using the artificial neural network. The optimal grasping cost function has been derived by constructing the cost function based on the probability density function of the normal distribution. Considering the characteristics of the object and the robot hand, the optimum height and width have been set to grasp the object by the robot hand. The resultant force between the contact area of the robot finger and the object has been estimated from the grasping force of the robot finger and the gravitational force of the object. In addition to these, the geometrical and gravitational center points of the object have been considered in obtaining the optimum grasping position of the robot finger and the object using the artificial neural network. To show the effectiveness of the proposed algorithm, the friction cone for the stable grasping operation has been modeled through the grasping experiments.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.4
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• pp.476-481
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• 2008

The necessity of intelligent surveillance system is gradually considered seriously from the space where the security is important. From this paper will load Network Camera in Mobile Robot based on embedded Linux and Goal is in the system embodiment will be able to track the intruder. From Network Camera uses Wireless Lan transmits an image with server, grasps direction of the intruder used Block Matching algorithms from server, transmits direction information and tracks an intruder. The robot tracks the intruder according to gets the effective image of an intruder. In compliance with this paper the system which is embodied is linked with a different surveillance system and as intelligent surveillance system there is a possibility of becoming worse a reliability.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.6 no.2
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• pp.161-166
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• 2006

Localization of mobile agent within a sensing network is a fundamental requirement for many applications, using networked navigating systems such as the sonar-sensing system or the visual-sensing system. To fully utilize the strengths of both the sonar and visual sensing systems, This paper describes a networked sensor-based navigation method in an indoor environment for an autonomous mobile robot which can navigate and avoid obstacle. In this method, the self-localization of the robot is done with a model-based vision system using networked sensors, and nonstop navigation is realized by a Kalman filter-based STSF(Space and Time Sensor Fusion) method. Stationary obstacles and moving obstacles are avoided with networked sensor data such as CCD camera and sonar ring. We will report on experiments in a hallway using the Pioneer-DX robot. In addition to that, the localization has inevitable uncertainties in the features and in the robot position estimation. Kalman filter scheme is used for the estimation of the mobile robot localization. And Extensive experiments with a robot and a sensor network confirm the validity of the approach.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.1601-1604
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• 1997

The most important topic in research of mobile robot is path planning in order to avoid with obstacle. In this study the path planning algorithm using a neural network model is proposed. The inputs of neural network are range data which are acquired form laser range finderm and weights are based on difference with goal direction. The thresholds are made by consdiering the marginal distance between mobile robot and obstacle. Consequently the outputs are obtained by multiplying input and weight. The obtained heading directiion enables the mobile robot to approach the goal, without any collision with obstacles around. The effectiveness of the this method of real-time navigation of a mobile robot is estimated by computer simulation in complex environment.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.74-74
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• 2000

This paper presents a neural network controller for a rigid-link electrically-driven robot. The proposed controller is designed in conjunction with three neural networks approximating for complicated nonlinear functions. Particularly, the fact, different from conventional schemes, is that the neural network based current observer is used. Therefore, no accurate measurement of the actuator driving current is required. In the proposed controller-observer scheme, the derived weight update rule guarantees the stability of closed-loop system in the sense of Lyapunov. The effectiveness and performance of the proposed method are demonstrated through computer simulation.

• Journal of Institute of Control, Robotics and Systems
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• v.10 no.5
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• pp.464-474
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• 2004

This paper proposes a middleware structure for the module-based personal robot, which can run on heterogeneous network interfaces and provides users easy interface-method regardless of underlying heterogeneous interfaces and convenient exchange of modules. The proposed middleware is divided into three layers of a streaming layer (SL), a network adaptation layer (NAL) and a network interface layer (NIL). The streaming layer manages application transactions using middleware services and provides user a uniform interfaces to the proposed middleware. The network adaptation layer manages a message-routing and provides naming service and it is a core of the proposed middleware. And the network interfaces layer manages dependent parts of heterogeneous network interfaces such as IEEE1394, USB, Ethernet, and CAN (Control Area Network). This paper implements the proposed middleware structure, where 3 types of interfaces of IEEE 1394, USB and Ethernet are used, and measures response times among those interfaces.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.29B no.10
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• pp.37-45
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• 1992

For a precise manipulator control in the presence of environmental uncertainties, it has long been recognized that the robot should be controlled in a task-referenced space. In this respect, an effective visual servo control system for robot manipulators based on neural networks is proposed. In the proposed control system, a Backpropagation neural network is used first to learn the mapping relationship between the robot's joint space and the video image space. However, in the real control loop, this network is not used in itself, but its first and second derivatives are used to generate servo commands for the robot. Second, and Adaline neural network is used to identify the approximately linear dynamics of the robot and also to generate the proper joint torque commands. Computer simulation has been performed demonstrating the proposed method's superior performance. Futrhermore, the proposed scheme can be effectively utilized in a robot skill acquisition system where the robot can be taught by watching a human behavioral task.

• Journal of the Korean Society of Industry Convergence
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• v.25 no.2_2
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• pp.193-198
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• 2022

In this paper, we introduce the improved control method are communicated between a master and a slave robot in the teleoperation systems. When the master and slave robots are located in different places, time delay is unavoidable under the network environment and it is well known that the system can become unstable when even a small time delay exists in the communication channel. The time delay may cause instability in teleoperation systems especially if those systems include haptic feedback. This paper presents a control scheme based on the estimator with virtual master model in teleoperation systems over the network. As the behavior of virtual model is tracking the one of master model, the operator can control real master robot by manipulating the virtual robot. And LQG/LTR scheme was adopted for the compensation of un-modeled dynamics. The approach is based on virtual master model, which has been implemented on a robot over the network. Its performance is verified by the computer simulation and the experiment.