• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1992년도 한국자동제어학술회의논문집(국제학술편); KOEX, Seoul; 19-21 Oct. 1992
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• pp.473-478
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• 1992

This paper aims to investigate the navigation control of a mobile robot in a confined environment. Steering angle becomes control variable which is computed from the fuzzy control rules. The identification method proposed in this paper presents the fuzzy control rules obtained through modelling of. the driving actions of human operator. The feasibility of the proposed method is evaluated through the application of the identified fuzzy controls rules to the navigation control of a mobile robot which follows the center of a corridor.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1997년도 하계학술대회 논문집 B
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• pp.403-405
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• 1997

In this paper, a navigation system based on fuzzy logic controllers is developed for a mobile robot in an unknown environment. The structure of this fuzzy navigation system features sensor system, fuzzy controllers for motion planning and the motion control system for real-time execution.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제6권4호
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• pp.293-298
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• 2006

In this paper, a sensor fusion based robot navigation method for the autonomous control of a miniature human interaction robot is presented. The method of navigation blends the optimality of the Fuzzy Neural Network(FNN) based control algorithm with the capabilities in expressing knowledge and learning of the networked Intelligent Robotic Space(IRS). States of robot and IR space, for examples, the distance between the mobile robot and obstacles and the velocity of mobile robot, are used as the inputs of fuzzy logic controller. The navigation strategy is based on the combination of fuzzy rules tuned for both goal-approach and obstacle-avoidance. To identify the environments, a sensor fusion technique is introduced, where the sensory data of ultrasonic sensors and a vision sensor are fused into the identification process. Preliminary experiment and results are shown to demonstrate the merit of the introduced navigation control algorithm.

• 전기학회논문지
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• 제63권1호
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• pp.108-114
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• 2014

This paper proposes a generation method of a navigation guidance direction and a fuzzy controller to achieve the autonomous navigation of a mobile robot using a particle swarm optimization(PSO) scheme in unknown environments. The proposed navigation guidance direction is the direction that leads a mobile robot to arrive a target point simultaneously with avoiding obstacles efficiently according to the surrounding local informations. It is generated by selecting the most suitable direction of the many directions in the surrounding environment using a particle swarm optimization scheme. Also, a robot can reach a target point with avoiding the various obstacles by controlling the robot so that it can move from its current orientation to the navigation guidance direction using the proposed fuzzy controller. Simulation results are presented to show the feasibility and validity of the proposed robot navigation scheme.

• 제어로봇시스템학회논문지
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• 제5권3호
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• pp.338-343
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• 1999

In this paper, the fuzzy neural network for the obstacle avoidance, which consists of the straight-line navigation and the barrier elusion navigation, is proposed and examined. For the straight-line navigation, the fuzzy neural network gets two inputs, angle and distance between the line and the mobile robot, and produces one output, steering velocity of the mobile robot. For the barrier elusion navigation, four ultrasonic sensors measure the distance between the barrier and the mobile robot and provide the distance information to the network. Then the network outputs the steering velocity to navigate along the obstacle boundary. Training of the proposed fuzzy neural network is executed in a given environment in real-time. The weights adjusting uses the back-propagation of the gradient of error to be minimized. Computer simulations are carried out to examine the efficiency of the real time learning and the guiding ability of the proposed fuzzy neural network. It has been shown that the mobile robot that employs the proposed fuzzy neural network navigates more safely with and less trembling locus compared with the previous reported efforts.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 1998년도 추계종합학술대회 논문집
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• pp.433-436
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• 1998

The objective of this paper is to design a adaptive fuzzy controller for autonomous navigation of mobile robot. The adaptive fuzzy controller has an advantage in data processing time and convergence speed. The basic idea of control is to induct membership function and fuzzy inference rules and to scale inducted membership function to suitable robot state. The adaptive fuzzy control method is applied to mobile robot and the simulation results show the effectiveness of our controller.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1997년도 춘계학술대회 학술발표 논문집
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• pp.141-144
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• 1997

This paper presents a method of navigation sign recognition in indoor environments using a fuzzy inference for an autonomous mobile robot. In order to adapt to image deformation of a navigation sign resulted from variations of view-points and distances, a multi-labeled template matching(MLTM) method and a dynamic area search method(DASM) are proposed. The DASM is proposed to detect correct feature points among incorrect feature points. Finally sugeno-style fuzzy inference are adopted for recognizing the navigation sign.

• 한국지능시스템학회논문지
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• 제11권5호
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• pp.433-440
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• 2001

본 논문에서는 다변수 퍼지 제어 시스템 이용한 선박의 자동 항해 시스템을 제안한다. 제안된 다변수 퍼지 제어 시스템은 세 개의 인력과 두 개의 출력을 가지는 서브시스템으로 구성되어지며, 제안된 시스템의 효과성을 증명하기 시뮬레이션을 통해 동적인 환경에서도 스스로 장애물을 인식하고 회피할 수 있음을 보였다.

• 한국항해항만학회:학술대회논문집
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• 한국항해항만학회 1994년도 춘계학술발표회
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• pp.55-67
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• 1994

Studies on the ship's automatic navigation & berthing control have been continued by way of solving the ship's mathematical model but the results of such studies have not reached to our satisfactory level due to its non-linear characteristics ar low speed. In this paper the authors propose a new berthing control system which can evaluate as closely as captain's decision-making by using the FNN(Fuzzy Neural Network) controller which can simulate captain's decision-making by using the FNN(Fuzzy neural Network) controller which can simulate captain's knowledge. This berthing controller consists of the navigation subsystem FNN controller and the berthing subsystem FNN controller. The learning data are drawn from Ship Handling Simulator (NavSim NMS90 MK III) and represent the ship motion characteristics internally According to learning procedure both FNN controllers can tune membership functions and identify fuzzy control rules automatically The verified results show the FNN controllers effective to incorporate captain's knowledge and experience of berthing.

• 로봇학회논문지
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• 제5권1호
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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.