• 제어로봇시스템학회:학술대회논문집
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• 1989.10a
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• pp.1120-1124
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• 1989

• The Journal of Korea Robotics Society
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• v.3 no.4
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• pp.345-350
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• 2008

This study aims to propose the concept design of oil spill protection robot which can rapidly intervene to control the oil spillage situation at the sea. Taking into account the fact that a huge amount of oil is transported trans-continentally by oil tanker, none of industrialized countries are completely safe from the marine oil spill which results in social, economical and ecological damages to their communities. The employment of double hull-oil tanker, pipe line transporting can be most safe way. Yet complete prevention of oil spill is probably not realistic. Accordingly the alternative solution to control marine oil spill and minimize the damages caused by the incident using intelligent robot technology based on swarm control method is proposed. The main features of oil spill protection(OSP) robot is explained via following three perspectives. Firstly, from functional point of view, OSP robot system safely and efficiently replaces oil boom installation manually conducted by human workers with intelligent robot technology based on swarm control theory. For second, its modular architecture brings efficient storage of main components including oil boom and facilitates maintenance. For the last, its geometric form and shape enables whole system to be installed to helicopter, boat or oil tanker itself with ease and to rapidly deploy the units to the oil spill area.

• Journal of Institute of Control, Robotics and Systems
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• v.6 no.5
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• pp.404-414
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• 2000

This paper addresses the development of a 3-fingered humanoid robot hand system and a real-time grasp synthesis of multifingered robot hands to find grasp configurations which satisfy the force closure condition of arbitrary shaped objects. We propose a fast and efficient grasp synthesis algorithm for planar polygonal objects, which yields the contact locations on a given polygonal object to obtain a force closure grasp by the multifingered robot hand. For an optimum grasp and real-time computation, we develop the preference and the hibernation process and assign physical constraints of the humanoid hand to the motion of each finger. The preferences consist of each sublayer reflecting the primitive preference similar to the conditional behaviors of humans for given objectives and their arrangements are adjusted by the heuristics inspired from human's grasping behaviors. The proposed method reduces the computational time significantly at the sacrifice of global optimality, and enables the grasp posture to be changable within two-finger and three-finger grasps. The performance of the presented algorithm is evaluated via simulation studies to obtain the force-closure grasps of polygonal objects with fingertip grasps. The architecture suggested is verified through experimental implementation to our robot hand system by solving the 2- or 3-finger grasp synthesis.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.1804-1807
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• 1997

This paper presents a new approach to the design of cruise control system of a mobile robot with two drive wheel. The proposed control scheme uses a Gaussian function as a unit function in the fuzzy-neural network, and back propagation algorithm to train the fuzzy-neural network controller in the framework of the specialized learnign architecture. It is proposed a learning controller consisting of two neural networks-fuzzy based on independent reasoning and a connecton net with fixed weights to simply the neural networks-fuzzy. The performance of the proposed controller is shown by performing the computer simulation for trajectory tracking of the speed and azimuth of a mobile robot driven by two independent wheels.

• Journal of the Korean Society for Precision Engineering
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• v.20 no.7
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• pp.155-162
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• 2003

We propose a new technique far real-tine controller design of a autonomous cruise mobile robot with three drive wheels. The proposed control scheme uses a Caussian function as a unit function in the fuzzy neural network. and a back propagation algorithm to train the fuzzy neural network controller in the framework of the specialized learning architecture. It is proposed a learning controller consisting of two neural network-fuzzy based on independent reasoning and a connection net with fixed weights to simply the neural networks-foray. The control performance of the proposed controller is illustrated by performing the computer simulation for trajectory tracking of the speed and azimuth of a autonomous cruise mobile robot driven by three independent wheels.

• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.68.1-68
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• 2002

This paper considers a rough neurocomputing approach to the design of the classify layer of a Brooks architecture for a robot control system. The Paradigm for neurocomputing that has its roots in rough set theory, and works well in cases where there is uncertainty about the values of measurements used to make decisions. In the case of the line-crawling robot (LCR) described in this paper, rough neurocomputing is used to classify sometimes noisy signals from sensors. The LCR is a robot designed to crawl along high-voltage transmission lines where noisy sensor signals are common because of the electromagnetic field surrounding conductors. In rough neurocomputing, training a network of neurons...

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2003.06a
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• pp.1697-1700
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• 2003

We propose a new technique for real-time controller design of a autonomous cruise mobile robot with three drive wheels. The proposed control scheme uses a Gaussian function as a unit function in the fuzzy neural network, and a back propagation algorithm to train the fuzzy neural network controller in the framework of the specialized learning architecture. It is proposed a learning controller consisting of two neural network-fuzzy based on independent reasoning and a connection net with fixed weights to simply the neural networks-fuzzy. The control performance of the proposed controller is illustrated by performing the computer simulation for trajectory tracking of the speed and azimuth of a autonomous cruise mobile robot driven by three independent wheels.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2003.04a
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• pp.312-318
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• 2003

We propose a new technique for the cruise control system design of a mobile robot with three drive wheel. The proposed control scheme uses a Gaussian function as a unit function in the fuzzy neural network and back propagation algorithm to train the fuzzy neural network controller in the framework of the specialized teaming architecture. It is proposed a learning controller consisting of too neural network-fuzzy based on independent reasoning and a connection net with fixed weights to simply the neural networks-fuzzy. The performance of the proposed controller is shown by performing the computer simulation for trajectory tracking of the speed and azimuth of a mobile robot driven by three independent wheels.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2004.10a
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• pp.207-212
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• 2004

This paper presents a new approach to the design of cruise control system of a mobile robot with two drive wheel. The proposed control scheme uses a Gaussian function as a unit function in the fuzzy neural network, and back propagation algorithm to train the fuzzy neural network controller in the framework of the specialized learning architecture. It is proposed a learning controller consisting of two neural network-fuzzy based on independent reasoning and a connection net with fixed weights to simply the neural networks-fuzzy. The performance of the proposed controller is shown by performing the computer simulation for trajectory tracking of the speed and azimuth of a mobile robot driven by two independent wheels.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.4
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• pp.485-492
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• 2006

This paper deals with implementation of network for distributed control system of a humanoid robot ISHURO(Inha Semyung Humanoid Robot). A humanoid robot needs much degree of freedom structurally and much data for having flexible movement. To realize such a humanoid robot, distributed control method is preferred to the centralized one since it gives a compactness, modularity and flexibility for the controllers. For organizing distributed control system of a humanoid robot, a control processor on a board is needed to individually control the joint motor and communication technology between the processors is required to transmit its information within control time. The processor is DSP-based processor and includes CAN network on a chip. It shares the computational load such as monitoring the sensor information and controlling the actuator between each of modules. In this paper, the communication architecture is suggested and its message protocol are discussed including message structure, time consumption for transmission, and controller structure at the view of distributed control for a humanoid robot. All of the sequence are simulated with Matlab and then verified with real walking experiment by ISHURO.