• International Journal of Fuzzy Logic and Intelligent Systems
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• v.2 no.1
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• pp.1-8
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• 2002

It has been integrated into several navigation systems. This paper shows that system recognizes difficult indoor roads without any specific marks such as painted guide line or tape. In this method the robot navigates with visual sensors, which uses visual information to navigate itself along the read. The Neural Network System was used to learn driving pattern and decide where to move. In this paper, I will present a vision-based process for AMR(Autonomous Mobile Robot) that is able to navigate on the indoor read with simple computation. We used a single USB-type web camera to construct smaller and cheaper navigation system instead of expensive CCD camera.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.45-45
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• 2000

This paper proposes a remote control system that combines computer network and an autonomous mobile robot. We control remotely an autonomous mobile robot with vision via the internet to guide it under unknown environments in the real time. The main feature of this system is that local operators need a World Wide Web browser and a computer connected to the internet communication network and so they can command the robot in a remote location through our Home Page. The hardware architecture of this system consists of an autonomous mobile robot, workstation, and local computers. The software architecture of this system includes the server part for communication between user and robot and the client part for the user interface and a robot control system. The server and client parts are developed using Java language which is suitable to internet application and supports multi-platform. Furthermore, this system offers an image compression method using motion JPEG concept which reduces large time delay that occurs in network during image transmission.

• Proceedings of the KIEE Conference
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• 2006.10c
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• pp.527-529
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• 2006

In this paper, we find a robot's path using a Virtual Reality Modeling Language and overlay vision. For correct robot's path we describe a method for localizing a mobile robot in its working environment using a vision system and VRML. The robt identifies landmarks in the environment, using image processing and neural network pattern matching techniques, and then its performs self-positioning with a vision system based on a well-known localization algorithm. After the self-positioning procedure, the 2-D scene of the vision is overlaid with the VRML scene. This paper describes how to realize the self-positioning, and shows the overlap between the 2-D and VRML scenes. The method successfully defines a robot's path.

• Proceedings of the KIEE Conference
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• 2003.11b
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• pp.103-106
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• 2003

In this paper, we present a hybrid inertial and vision-based tracking system for VR applications. One of the most important aspects of VR (Virtual Reality) is providing a correspondence between the physical and virtual world. As a result, accurate and real-time tracking of an object's position and orientation is a prerequisite for many applications in the Virtual Environments. Pure vision-based tracking has low jitter and high accuracy but cannot guarantee real-time pose recovery under all circumstances. Pure inertial tracking has high update rates and full 6DOF recovery but lacks long-term stability due to sensor noise. In order to overcome the individual drawbacks and to build better tracking system, we introduce the fusion of vision-based and inertial tracking. Sensor fusion makes the proposal tracking system robust, fast, accurate, and low jitter and noise. Hybrid tracking is implemented with Kalman Filter that operates in a predictor-corrector manner. Combining bluetooth serial communication module gives the system a full mobility and makes the system affordable, lightweight energy-efficient. and practical. Full 6DOF recovery and the full mobility of proposal system enable the user to interact with mobile device like PDA and provide the user with natural interface.

• Proceedings of the KIEE Conference
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• 2007.04a
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• pp.6-8
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• 2007

In this paper, We propose reasoning system for object recognition and space classification using not only visual features but also contextual information. It is necessary to perceive object and classify space in real environments for mobile robot. especially vision based. Several visual features such as texture, SIFT. color are used for object recognition. Because of sensor uncertainty and object occlusion. there are many difficulties in vision-based perception. To show the validities of our reasoning system. experimental results will be illustrated. where object and space are inferred by bi -directional rules even with partial and uncertain information. And the system is combined with top-down and bottom-up approach.

• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.1
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• pp.49-54
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• 2011

In this paper, we represent a mobile robot which can recognize chemical odor, measure concentration, and track its source indoors. The mobile robot has the function of smell that can sort several gases in experiment such as ammonia, ethanol, and their mixture with neural network algorithm and measure each gas concentration with fuzzy rules. In addition, it can not only navigate to the desired position with vision system by avoiding obstacles but also transmit odor information and warning messages earned from its own operations to other nodes by multi-hop communication in wireless sensor network. We suggest the way of odor sorting, concentration measurement, and source tracking for a mobile robot in wireless sensor network using a hybrid algorithm with vision system and gas sensors. The experimental studies prove that the efficiency of the proposed algorithm for odor recognition, concentration measurement, and source tracking.

• Proceedings of the IEEK Conference
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• 2002.06d
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• pp.291-294
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• 2002

A simple computer vision technology to measure the middle-ranged depth with mono camera and plain mirror is proposed The proposed system is structured wiか the rotating mirror in front of the fixed mono camera In contrast to the previous stereo vision system in which the disparity of the closer object is larger than that of the distant object, the pixel movement caused by the rotating mirror is bigger for the pixels of the distant object in the proposed system Being inspired by such feature in the proposed system the principle of the depth measurement based on the relation of the pixel movement and the distance of object have been investigated. Also, the factors to influence the precision of the measurement are analysed The benefits of the proposed system are low price and less chance of occlusion. The robustness for practical usage is an additional benefit of the proposed vision system.

• Proceedings of the KIEE Conference
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• 2007.10a
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• pp.317-318
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• 2007

One of the important factors of the autonomous mobile robot is to build a map for surround environment and estimate its localization. This paper suggests a sensor fusion method of laser range finder and monocular vision sensor for the simultaneous localization and map building. The robot observes the comer points in the environment as features using the laser range finder, and extracts the SIFT algorithm with the monocular vision sensor. We verify the improved localization performance of the mobile robot from the experiment.

• Proceedings of the KIEE Conference
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• 1996.07b
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• pp.1251-1253
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• 1996

The vision processing system for mobile robots requires real time processing and reliability for the purpose of safe navigation. But, general types of vision systems are not appropriate owing to the correspondence problem which correlates the points out of two images. To determine the obstacle area, we use correspondences of line segments between two perspective images sequentially acquired by camera. To simplify the correspondence, the matching of line segments are performed in the navigation space, based on the assumption that mobile robot should be navigated in the flat surface and the motion of mobile robot between two frames should be approximately known.

• Journal of Institute of Control, Robotics and Systems
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• v.14 no.2
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• pp.168-177
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• 2008

Path finding is a key element in the navigation of a mobile robot. To find a path, robot should know their position exactly, since the position error exposes a robot to many dangerous conditions. It could make a robot move to a wrong direction so that it may have damage by collision by the surrounding obstacles. We propose a method obtaining an accurate robot position. The localization of a mobile robot in its working environment performs by using a vision system and Virtual Reality Modeling Language(VRML). The robot identifies landmarks located in the environment. An image processing and neural network pattern matching techniques have been applied to find location of the robot. After the self-positioning procedure, the 2-D scene of the vision is overlaid onto a VRML scene. This paper describes how to realize the self-positioning, and shows the overlay between the 2-D and VRML scenes. The suggested method defines a robot's path successfully. An experiment using the suggested algorithm apply to a mobile robot has been performed and the result shows a good path tracking.