• International Journal of Control, Automation, and Systems
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• 제3권4호
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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.

• 한국콘텐츠학회논문지
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• 제5권5호
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• pp.85-91
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• 2005

최근 홈네트워크 시스템에 홈로봇이 적용되어 보다 다양한 서비스를 제공하고 있다. 홈로봇은 무선 홈 네트워크 시스템을 기반으로 자율주행을 하며 기본적으로 홈디바이스의 제어뿐만 아니라 홈모니터링을 통한 방범 방재 서비스 및 각종 엔터테인먼트 서비스를 수행한다. 하지만 기존의 홈로봇은 모든 기능을 로봇에 탑재하여 로봇 단말의 크기가 커지고 수행할 콘텐츠나 어플리케이션의 관리가 용이하지 못하며 새로운 기능 추가에 많은 어려움을 겪고 있다. 또한 로봇의 위치인식 기능에 있어서도 많은 개선점이 필요한 실정이다. 본 논문에서는 로봇의 복잡한 연산처리를 외부 디지털 디바이스에게 분담하여 로봇의 자원을 효율적으로 이용하고 새로운 기능에 대한 추가도 용이하도록 하며 RFID를 통해 로봇의 위치를 인식함으로써 보다 개선된 홈로봇 시스템을 제안한다.

• 로봇학회논문지
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• 제14권2호
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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.

• 한국지능시스템학회논문지
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• 제18권4호
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• pp.476-481
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• 2008

보안이 중요시되는 공간에서 지능형 감시 시스템의 필요성이 점차 중요시되고 있다. 본 논문에서는 embedded Linux 기반의 Mobile Robot에 Network Camera를 탑재하여 침입자를 추적할 수 있는 시스템 구현에 목적을 두고 있다. Network Camera부터 Wireless Lan을 이용하여 서버로 영상을 전송하고, 서버에서 블록매칭 알고리즘을 이용하여 침입자의 이동경로를 파악하며 침입자에 대한 방향 정보를 전송하여 침입자를 추적한다. 로봇이 침입자를 추적함에 따라 침입자의 유효 영상을 얻는다. 본 논문에 의해서 구현된 시스템은 다른 감시 시스템과 연동하여 지능형 감시 시스템으로서 신뢰성을 더할 수 있다.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제6권2호
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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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• 제어로봇시스템학회 1997년도 한국자동제어학술회의논문집; 한국전력공사 서울연수원; 17-18 Oct. 1997
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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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• 제어로봇시스템학회 2000년도 제15차 학술회의논문집
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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.

• 제어로봇시스템학회논문지
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• 제10권5호
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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.

• 전자공학회논문지B
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• 제29B권10호
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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.

• 한국산업융합학회 논문집
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• 제25권2_2호
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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.