• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.59.6-59
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• 2001

A virtual obstacle method for escaping local minima encountered by sonar-based mobile robot navigation used in real-time obstacle avoidance is presented. The new algorithm judges the mobile robot falls into local minima and helps the mobile robot escape from Et, which regards a concave obstacle as convex or flat one, virtual obstacle method. In the algorithm, it starts to make virtual-obstacle when the mobile robot meets a certain condition, then the robot mores back slowly taking inside area of local minima as obstacle gradually The new algorithm is simulated. The experimental results are presented to demonstrate the usefulness of the method.

• Journal of Institute of Control, Robotics and Systems
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• v.17 no.7
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• pp.651-658
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• 2011

An efficient modified $D^*$ lite algorithm is suggested, which can perform online path planning for mobile robots in dynamic indoor environment. Online path planning should plan and execute alternately in a short time, and hence it enables the robot avoid unknown dynamic obstacles which suddenly appear on robot's path. Based on $D^*$ Lite algorithm, we improved representation of edge cost, heuristic function, and priority queue management, to build a modified $D^*$ Lite algorithm. Performance of the proposed algorithm is revealed via extensive simulation study.

• The Magazine of the IEIE
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• v.23 no.12
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• pp.115-125
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• 1996

This paper presents a new algorithm for the self-localization of a mobile robot using one degree perspective Invariant(Cross Ratio). Most of conventional model-based self-localization methods have some problems that data structure building, map updating and matching processes are very complex. Use of a simple cross ratio can be effective to the above problems. The algorithm is based on two basic assumptions that the ground plane is flat and two locally parallel sloe-lines are available. Also it is assumed that an environmental map is available for matching between the scene and the model. To extract an accurate steering angle for a mobile robot, we take advantage of geometric features such as vanishing points. Feature points for cross ratio are extracted robustly using a vanishing point and intersection points between two locally parallel side-lines and vertical lines. Also the local position estimation problem has been treated when feature points exist less than 4points in the viewed scene. The robustness and feasibility of our algorithms have been demonstrated through real world experiments In Indoor environments using an indoor mobile robot, KASIRI-II(KAist Simple Roving Intelligence).

• Journal of the Korean Society for Precision Engineering
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• v.26 no.8
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• pp.89-95
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• 2009

In this paper, a navigation algorithm is proposed using RFID tags in indoor environments. Firstly, a stochastic sensor model of RFID is derived and the design factors including the maximum identifiable distance, the identification direction and the read success rate are obtained through experiments. The obstacle avoidance algorithm is developed with consideration of those factors for a variety of RFID antenna configurations and different indoor environments. The algorithm is tested by computer simulations and implemented on a mobile robot.

• The Journal of Korea Robotics Society
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• v.3 no.1
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• pp.33-41
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• 2008

Based on object recognition technology, we present a new global localization method for robot navigation. For doing this, we model any indoor environment using the following visual cues with a stereo camera; view-based image features for object recognition and those 3D positions for object pose estimation. Also, we use the depth information at the horizontal centerline in image where optical axis passes through, which is similar to the data of the 2D laser range finder. Therefore, we can build a hybrid local node for a topological map that is composed of an indoor environment metric map and an object location map. Based on such modeling, we suggest a coarse-to-fine strategy for estimating the global localization of a mobile robot. The coarse pose is obtained by means of object recognition and SVD based least-squares fitting, and then its refined pose is estimated with a particle filtering algorithm. With real experiments, we show that the proposed method can be an effective vision- based global localization algorithm.

• Proceedings of the KIEE Conference
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• 2002.07d
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• pp.2421-2423
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• 2002

A global ultrasonic sensor system for self-localization of an indoor mobile robot is proposed in this paper. By the global ultrasonic sensor system, it is meant several ultrasonic transmitters fixed at some positions in the world coordinate and the receiver in the moving coordinate of a mobile robot. In order to achieve the synchronization between an ultrasonic transmitter and receiver and to avoid the crosstalk among the ultrasonic transmitters, simple radio frequency transmitters and receivers are adopted. Experiments are carried out to verify the effectiveness of the proposed ultrasonic sensor system.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1456-1459
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• 2005

To compensate the drawbacks, a new localization method that estimates the global position of the mobile robot by using a camera set on ceiling in the corridor is proposed. This scheme is not a relative localization, which decreases the position error through algorithms with noisy sensor data. The effectiveness of the proposed localization scheme is demonstrated by the experiments.

• The Journal of Korea Robotics Society
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• v.18 no.4
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• pp.487-495
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• 2023

This paper presents a method for autonomous exploration of indoor environments using a 2-dimensional Light Detection And Ranging (LiDAR) scanner. The proposed frontier-based exploration method considers navigability from the current robot position to extracted frontier targets. An approach to constructing the point cloud grid map that accurately reflects the occupancy probability of glass obstacles is proposed, enabling identification of safe frontier grids on the safety grid map calculated from the point cloud grid map. Navigability, indicating whether the robot can successfully navigate to each frontier target, is calculated by applying the skeletonization-informed rapidly exploring random tree algorithm to the safety grid map. While conventional exploration approaches have focused on frontier detection and target position/direction decision, the proposed method discusses a safe navigation approach for the overall exploration process until the completion of mapping. Real-world experiments have been conducted to verify that the proposed method leads the robot to avoid glass obstacles and safely navigate the entire environment, constructing the point cloud map and calculating the navigability with low computing time deviation.

• Journal of Institute of Control, Robotics and Systems
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• v.19 no.5
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• pp.468-472
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• 2013

This paper deals with the indoor localization problem for a SSMR (Skid Steering Mobile Robot) subjected to wheel-ground friction and with one LRF (Laser Range Finder). In order to compensate for some friction effect, a friction related coefficient is estimated by the recursive least square algorithm and appended to the maneuvering command. Also to reduce odometric information based localization errors, the lines are extracted with scan points of LRF and matched with the ones of the corresponding map built in advance. The present friction compensation and scan map matching schemes have been applied to a laboratory SSMR, and experimental results are given to validate the localization performance along an indoor corridor.

• The Journal of Korea Robotics Society
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• v.2 no.1
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• pp.40-47
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• 2007

Recently, with the development of service robots and with the new concept of ubiquitous world, the position estimation of mobile objects has been raised to an important problem. As pre-liminary research results, some of the localization schemes are introduced, which provide the absolute location of the moving objects subjected to large errors. To implement a precise and convenient localization system, a new absolute position estimation method for a mobile robot in indoor environment is proposed in this paper. Design and implementation of the localization system comes from the usage of active beacon systems (based upon RFID technology). The active beacon system is composed of an RFID receiver and an ultra-sonic transmitter: 1. The RFID receiver gets the synchronization signal from the mobile robot and 2. The ultra-sonic transmitter sends out the traveling signal to be used for measuring the distance. Position of a mobile robot in a three dimensional space can be calculated basically from the distance information from three beacons and the absolute position information of the beacons themselves. Since it is not easy to install the beacons at a specific position precisely, there exists a large localization error and the installation time takes long. To overcome these problems, and provide a precise and convenient localization system, a new auto calibration algorithm is developed in this paper. Also the extended Kalman filter has been adopted for improving the localization accuracy during the mobile robot navigation. The localization accuracy improvement through the proposed auto calibration algorithm and the extended Kalman filter has been demonstrated by the real experiments.