• International Journal of Precision Engineering and Manufacturing
• /
• 제6권3호
• /
• pp.29-36
• /
• 2005

With the rapid growth of the Internet, the Internet-based robot has been realized by connecting off-line robot to the Internet. However, because the Internet is often irregular and unreliable, the varying time delay in data transmission is a significant problem for the construction of the Internet-based robot system. Thus, this paper is concerned with the development of an Internet-based robot system, which is insensitive to the Internet time delay. For this purpose, the PPS (Position Prediction Simulator) is suggested and implemented on the system. The PPS consists of two parts : the robot position prediction part and the projective virtual scene part. In the robot position prediction part, the robot position is predicted for more accurate operation of the mobile robot, based on the time at which the user's command reaches the robot system. The projective virtual scene part shows the 3D visual information of a remote site, which is obtained through image processing and position prediction. For the verification of this proposed PPS, the robot was moved to follow the planned path under the various network traffic conditions. The simulation and experimental results showed that the path error of the robot motion could be reduced using the developed PPS.

• 제어로봇시스템학회:학술대회논문집
• /
• 제어로봇시스템학회 1991년도 한국자동제어학술회의논문집(국내학술편); KOEX, Seoul; 22-24 Oct. 1991
• /
• pp.861-865
• /
• 1991

In general, the fusion of neural network and fuzzy logic theory is based on the fact that neural network and fuzzy logic theory have the common properties that 1) the activation function of a neuron is similar to the membership function of fuzzy variable, and 2) the functions of summation and products of neural network are similar to the Max-Min operator of fuzzy logics. In this paper, a fuzzy-neural network will be proposed and a force controller of the robot gripper, utilizing the fuzzy-neural network, will be presented. The effectiveness of the proposed strategy will be demonstrated by computer simulation.

• 한국지능시스템학회논문지
• /
• 제16권4호
• /
• pp.485-492
• /
• 2006

본 논문은 휴머노이드 로봇 ISHURO의 분산 제어를 위한 네트웍 구현에 대해 다루고 있다. 일반적으로 휴머노이드형 로봇은 기구학적으로 유연한 동작을 위해 다수의 자유도가 필요하다. 이를 구현하기 위해서는 중앙에서 일괄적으로 처리 하는 것 보다 간결 하면서도 유연성을 줄 수 있는 분산 처리 방법이 선호되고 있다. 분산 처리를 위한 제어기를 구성할 때는 로봇의 모터를 독립적으로 제어하기 위한 제어기가 별도로 필요하며 모듈 간에는 정해진 시간 내에 데이터를 교환할 수 있는 통신 기법이 필요하다. ISHURO의 각 관절은 자체 내에 독립된 DSP를 내장하고 있으며 CAN 네트웍을 이용하여 모듈간의 통신을 하여 구동기를 재어하거나 센서의 값을 모니터링 할 수 있게 되어 있다. 본 논문에서는 이를 위한 통신 구조를 제안하고 필요한 전송 메시지를 정의하고, 전송시간을 분석하여 로봇 분산 제어기 구조에 적절한 전송 프로토콜을 제시하였다. 모든 과정은 Matlab을 이용하여 컴퓨터모의실험을 수행하였고 실제 휴머노이드 로봇에 적용하여 보행실험을 통해 검증 하였다.

• 제어로봇시스템학회:학술대회논문집
• /
• 제어로봇시스템학회 2005년도 ICCAS
• /
• pp.2254-2259
• /
• 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 advantages of neural network, fuzzy model and wavelet transform. The basic idea of our WFNN structure is to realize the process of fuzzy reasoning of 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 mobile robot using the gradient descent(GD) method. In addition, an approach that uses adaptive learning rates for the training of WFNN controller is driven via a Lyapunov stability analysis to guarantee the 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 performance of the WFNN controller with those of the FNN, the WNN and the WFM controllers.

• 로봇학회논문지
• /
• 제3권4호
• /
• pp.278-286
• /
• 2008

Reliable functionalities for autonomous navigation and object recognition/handling are key technologies to service robots for executing useful services in human environments. A considerable amount of research has been conducted to make the service robot perform these operations with its own sensors, actuators and a knowledge database. With all heavy sensors, actuators and a database, the robot could have performed the given tasks in a limited environment or showed the limited capabilities in a natural environment. With the new paradigms on robot technologies, we attempted to apply smart environments technologies-such as RFID, sensor network and wireless network- to robot functionalities for executing reliable services. In this paper, we introduce concepts of proposed smart environments based robot navigation and object recognition/handling method and present results on robot services. Even though our methods are different from existing robot technologies, successful implementation result on real applications shows the effectiveness of our approaches.

• 제어로봇시스템학회:학술대회논문집
• /
• 제어로봇시스템학회 2000년도 제15차 학술회의논문집
• /
• pp.279-279
• /
• 2000

In this paper, we have proposed a new approach to the design of robot vision system to develop the technology for the automatic test and assembling of precision mechanical and electronic parts fur the factory automation. In order to perform real time implementation of the automatic assembling tasks in the complex processes, we have developed an intelligent control algorithm based-on neural networks control theory to enhance the precise motion control. Implementing of the automatic test tasks has been performed by the real-time vision algorithm based-on TMS320C31 DSPs. It distinguishes correctly the difference between the acceptable and unacceptable defective item through pattern recognition of parts by the developed vision algorithm. Finally, the performance of proposed robot vision system has been illustrated by experiment for the similar model of fifth cell among the twelve cell fur automatic test and assembling in S company.

• ETRI Journal
• /
• 제38권6호
• /
• pp.1229-1239
• /
• 2016

For efficient interaction between humans and robots, robots should be able to understand the meaning and intention of human behaviors as well as recognize them. This paper proposes an interactive human intention reading method in which a robot develops its own knowledge about the human intention for an object. A robot needs to understand different human behavior structures for different objects. To this end, this paper proposes a hierarchical behavior knowledge network that consists of behavior nodes and directional edges between them. In addition, a human intention reading algorithm that incorporates reinforcement learning is proposed to interactively learn the hierarchical behavior knowledge networks based on context information and human feedback through human behaviors. The effectiveness of the proposed method is demonstrated through play-based experiments between a human and a virtual teddy bear robot with two virtual objects. Experiments with multiple participants are also conducted.

• 한국지능시스템학회:학술대회논문집
• /
• 한국퍼지및지능시스템학회 2003년도 ISIS 2003
• /
• pp.368-371
• /
• 2003

A new accurate and reliable human-in-the-loop control by artificial neural network (ANN) for human assistance robot was proposed in this paper. The principle of human-in-the-loop control by ANN was explained including the system architecture of human assistance robot control the design of the controller the control process as well as the switching of the different control patterns. Based on the proposed method, the control of meal assistance robot was implemented. In the controller of meal assistance robote a feedforward ANN controller was designed for the accurate position control. For safety a feedback ANN forcefree control was installed in the meal assistance robot. Both controllers have taken fully into account the influence of human arm upon the meal assistance robote and they can be switched smoothly based on the external force induced by the challenged person arm. By the experimental and simulation work of this method for an actual meal assistance robote the effectiveness of the proposed method was verified.

• 대한전기학회논문지:시스템및제어부문D
• /
• 제53권1호
• /
• pp.32-39
• /
• 2004

The color-based vision systems have been used to recognize our team robots, the opponent team robots and a ball in the robot soccer system. The color-based vision systems have the difficulty in that they are very sensitive to color variations brought by brightness changes. In this paper, a neural network trained with data obtained from various illumination conditions is used to classify colors in the modified YUV color space for the robot soccer vision system. For this, a new method to measure brightness is proposed by use of a color card. After the neural network is constructed, a look-up-table is generated to replace the neural network in order to reduce the computation time. Experimental results show that the proposed color classification method is robust under illumination variations.

• 제어로봇시스템학회:학술대회논문집
• /
• 제어로봇시스템학회 1996년도 한국자동제어학술회의논문집(국내학술편); 포항공과대학교, 포항; 24-26 Oct. 1996
• /
• pp.698-701
• /
• 1996

A novel neural network control scheme is proposed to identify the inverse dynamic model of robot manipulator and to compensate for uncertainties in robot dynamics. The proposed controller is called reference compensation technique(RCT) by compensating at reference input trajectory. The proposed RCT scheme has many benefits due to the differences in compensating position and learning algorithm. Since the compensation is done outside the plant it can be applied to many control systems without modifying the inside controller. It performs well with low controller gain because the operating range of input values is small and the output of the neural network controller is amplified through the controller gain. The back-propagation algorithm is used to train and simulations of three link robot manipulator are carried out to prove the proposed controller's performances.