• The Transactions of the Korean Institute of Electrical Engineers P
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• v.60 no.2
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• pp.63-67
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• 2011

This paper presents the mobile robot control based on vision system. The proposed vision based controller consist of the camera tracking controller and the formation controller. Th e camera controller has the adaptive gain based on IBVS. The formation controller which is designed in the sense of the Lyapunov stability follows the leader. Simluation results show that the proposed vision based mobile robot control is validated for indoor mobile robot applications.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2003.06a
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• pp.915-919
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• 2003

This paper presents a fuzzy-motion-control based tracking algorithm of mobile robots, which uses the geometrical information derived from the active stereo-vision system mounted on the mobile robot. The active stereo-vision system consists of two color cameras that rotates in two angular dimensions. With the stereo-vision system, the center position and depth information of the target object can be calculated. The proposed fuzzy motion controller is used to calculate the tracking velocity and angular position of the mobile robot, which makes the mobile robot keep following the object with a constant distance and orientation.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.540-543
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• 1997

In this paper, a precise trajectory tracking method for mobile robot using a vision system is presented. In solving the problem of precise trajectory tracking, a hierarchical control structure is used which is composed of the path planer, vision system, and dynamic controller. When designing the dynamic controller, non-ideal conditions such as parameter variation, frictional force, and external disturbance are considered. The proposed controller can learn bounded control input for repetitive or periodic dynamics compensation which provides robust and adaptive learning capability. Moreover, the usage of vision system makes mobile robot compensate the cumulative location error which exists when relative sensor like encoder is used to locate the position of mobile robot. The effectiveness of the proposed control scheme is shown through computer simulation.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.249-249
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• 2000

This paper introduces a clean mobile robot fur 4th generation LCD cassette, which is guided by optical sensor and position compensation using vision module. The mobile robot for LCD cassette transfer might be controlled by AGV controller which has powerful algorithms. It offers optimum routes to the destination of clean mobile robot by using dynamic dispatch algorithm and MAP data. This clean mobile robot is equipped with 4 axes fork type manipulator providing repeatability accuracy of $\pm$ 0.05mm.

• Proceedings of the IEEK Conference
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• 2002.06c
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• pp.29-32
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• 2002

This paper suggest target detection algorithm for mobile robot control using color and shape recognition. In many cases, ultrasonic sensor(USS) is used in mobile robot system to measure the distance between obstacles. But with only USS, it may have many restrictions. So we attached CCD camera to mobile robot to overcome its restrictions. If visual information is given to robot system then robot system will be able to accomplish more complex mission successfully. With acquired vision data, robot looks for target by color and recognize its shape.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2002.10a
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• pp.554-557
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• 2002

This paper describes power potential field method for the collision-free path planning of stereo-vision based mobile robot. Area based stereo matching is performed for obstacle detection in uncertain environment. The repulsive potential is constructed by distributing source points discretely and evenly on the boundaries of obstacles and superposing the power potential which is defined so that the source potential will have more influence on the robot than the sink potential when the robot is near to source point. The mobile robot approaches the goal point by moving the robot directly in negative gradient direction of the main potential. We have investigated the possibility of power potential method for the collision-free path planning of mobile robot through various experiments.

• Journal of Sensor Science and Technology
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• v.20 no.3
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• pp.178-186
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• 2011

Line tracking is a well defined method of mobile robot navigation. It is simple in concept, technically easy to implement, and already employed in many industrial sites. Among several different line tracking methods, magnetic sensing is widely used in practice. In comparison, vision-based tracking is less popular due mainly to its sensitivity to surrounding conditions such as brightness and floor characteristics although vision is the most powerful robotic sensing capability. In this paper, a vision-based robust path line detection technique is proposed for the navigation of a mobile robot assuming uncontrollable surrounding conditions. The technique proposed has four processing steps; color space transformation, pixel-level line sensing, block-level line sensing, and robot navigation control. This technique effectively uses hue and saturation color values in the line sensing so to be insensitive to the brightness variation. Line finding in block-level makes not only the technique immune from the error of line pixel detection but also the robot control easy. The proposed technique was tested with a real mobile robot and proved its effectiveness.

• Journal of the Korean Institute of Intelligent Systems
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• v.11 no.6
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• pp.470-476
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• 2001

This paper presents a new algorithm of path planning and obstacle avoidance for autonomous mobile robots with vision system that is working in unknown environments. Distance variation technique is used in path planning to approach the target and avoid obstacles in work space as well . In this approach, the Sobel operator is employed to detect edges of obstacles and the distances between the mobile robot and the obstacles are measured. Fuzzy rules are used for trajectory planning and obstacle avoidance to improve the autonomy of mobile robots. It is shown by computer simulation that the proposed algorithm is superior to the vector field approach which sometimes traps the mobile robot into some local obstacles. An autonomous mobile robot with single vision is developed for experiments. We also show that the developed mobile robot with the proposed algorithm is navigating very well in complex unknown environments.

• Proceedings of the KIEE Conference
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• 1998.07g
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• pp.2200-2202
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• 1998

Autonomous mobile robot provide many functions such as sensing, processing, and driving. For more intelligent jobs, more intelligent functions are to be added and the existing functions may be updated. To execute a job autonomous mobile robot has a information of surrounding environment. So, robot uses sonar sensor, vision sensor and so on. Obtained sensor information is used map generation. This paper is focused on map generation using stereo vision system.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2003.06a
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• pp.934-937
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• 2003

In this paper, authors use a stereo vision system based on the visual model of human and establish inexpensive method that recognizes moving distance using characteristic points around the robot. With the stereovision. the changes of the coordinate values of the characteristic points that are fixed around the robot are measured. Self-displacement and self-localization recognition system is proposed from coordination reconstruction with those changes. To evaluate the proposed system, several characteristic points that is made with a LED around the robot and two cheap USB PC cameras are used. The mobile robot measures the coordinate value of each characteristic point at its initial position. After moving, the robot measures the coordinate values of the characteristic points those are set at the initial position. The mobile robot compares the changes of these several coordinate values and converts transformation matrix from these coordinate changes. As a matrix of the amount and the direction of moving displacement of the mobile robot, the obtained transformation matrix represents self-displacement and self-localization by the environment.