• Proceedings of the Korean Society of Precision Engineering Conference
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• 2005.06a
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• pp.1034-1039
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• 2005

SALM(Simultaneous localization and mapping) and AI(Artificial intelligence) have been active research areas in robotics for two decades. In particular, localization is one of the most important tasks in mobile robot research. Until now expensive sensors such as a laser sensor have been used for mobile robot localization. Currently, the proliferation of RFID technology is advancing rapidly, while RFID reader devices, antennas and tags are becoming increasingly smaller and cheaper. So, in this paper, the smart floor using passive RFID tags is proposed and, passive RFID tags are mainly used for identifying location of the mobile robot in the smart floor. We discuss a number of challenges related to this approach, such as tag distribution (density and structure), typing and clustering. In the smart floor using RFID tags, the localization error results from the sensing area of the RFID reader, because the reader just knows whether the tag is in the sensing range of the sensor and, until now, there is no study to estimate the heading of mobile robot using RFID tags. So, in this paper, two algorithms are suggested to. The Markov localization method is used to reduce the location(X,Y) error and the Kalman Filter method is used to estimate the heading($\theta$) of mobile robot. The algorithms which are based on Markov localization require high computing power, so we suggest fast Markov localization algorithm. Finally we applied these algorithms our personal robot CMR-P3. And we show the possibility of our probability approach using the cheap sensors such as odometers and RFID tags for mobile robot localization in the smart floor

• Journal of Institute of Control, Robotics and Systems
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• v.12 no.1
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• pp.47-53
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• 2006

Localization is a essential technology for mobile robot to work well. Until now expensive sensors such as laser sensors have been used for mobile robot localization. We suggest RFID tag based localization system. RFID tag devices, antennas and tags are cheap and will be cheaper in the future. The RFID tag system is one of the most important elements in the ubiquitous system and RFID tag will be attached to all sorts of goods. Then, we can use this tags for mobile robot localization without additional costs. So, in this paper, the smart floor using passive RFID tags is proposed and, passive RFID tags are mainly used for identifying mobile robot's location and pose in the smart floor. We discuss a number of challenges related to this approach, such as tag distribution (density and structure), typing and clustering. When a mobile robot localizes in this smart floor, the localization error mainly results from the sensing range of the RFID reader, because the reader just ran know whether a tag is in the sensing range of the sensor. So, in this paper, we suggest two algorithms to reduce this error. We apply the particle filter based Monte Carlo localization algorithm to reduce the localization error. And with simulations and experiments, we show the possibility of our particle filter based Monte Carlo localization in the RFID tag based localization system.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1797-1801
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• 2005

SLAM(Simultaneous localization and mapping) and AI(Artificial intelligence) have been active research areas in robotics for two decades. In particular, localization is one of the most important issues in mobile robot research. Until now expensive sensors like a laser sensor have been used for the mobile robot's localization. Currently, as the RFID reader devices like antennas and RFID tags become increasingly smaller and cheaper, the proliferation of RFID technology is advancing rapidly. So, in this paper, the smart floor using passive RFID tags is proposed and, passive RFID tags are mainly used to identify the mobile robot's location on the smart floor. We discuss a number of challenges related to this approach, such as RFID tag distribution (density and structure), typing and clustering. In the smart floor using RFID tags, because the reader just can senses whether a RFID tag is in its sensing area, the localization error occurs as much as the sensing area of the RFID reader. And, until now, there is no study to estimate the pose of mobile robot using RFID tags. So, in this paper, two algorithms are suggested to. We use the Markov localization algorithm to reduce the location(X,Y) error and the Kalman Filter algorithm to estimate the pose(q) of a mobile robot. We applied these algorithms in our experiment with our personal robot CMR-P3. And we show the possibility of our probability approach using the cheap sensors like odometers and RFID tags for the mobile robot's localization on the smart floor.

• IE interfaces
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• v.23 no.3
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• pp.239-245
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• 2010

This paper considers a localization problem of a moving tag on RFID(Radio Frequency Identification) systems, where a positioning engine collects TDOA(Time-difference of Arrival) signal from a target tag to estimate the position of the tag. To localize the tag in the RFID system, we develop two heuristic algorithms and evaluate their performance in the estimation error and computational time by using randomly generated numerical examples. Based upon the performance evaluation, we can conclude our algorithms are valuable for localization the moving target.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.10
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• pp.949-955
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• 2007

This paper proposes an efficient localization algorithm in the RFID sensor space for the precise localization of a mobile robot. The RFID sensor space consists of embedded sensors and a mobile robot. The embedded sensors, that is tags are holding the absolute position data and provide them to the robot which carries a reader and requests the absolute position fur localization. The reader, it is called as antenna usually, gets several tag data at the same time within its readable range. It takes time to read all the tags and to process the data to estimate the position, which is a major factor to deteriorate the localization accuracy. In this paper, an efficient algorithm to estimate the position and orientation of the mobile robot as quickly as possible has been proposed. Along with the algorithm, a new allocation of the tags in the RFID sensor space is also proposed to improve the localization accuracy. The proposed algorithms are demonstrated and verified through the real experiments.

• Korean Management Science Review
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• v.29 no.3
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• pp.1-11
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• 2012

This paper considers a localization problem in time difference of arrival (TDOA)-based radio frequency identification (RFID) systems. To estimate the position of a target tag, this paper suggests three localization algorithms that use benchmark tags. The benchmark tags are the same type as the target tag, but either the locations or distance of the benchmark tags are known. Two algorithms use the benchmarks for auxiliary information to improve the estimation accuracy of the other localization algorithms such as least squared estimator (LSE). The other one utilizes the benchmarks as essential tags to estimate the location. Numerical tests show that the localization accuracy can be improved by using benchmark tags especially when an algorithm using the LSE is applied to the localization problem. Furthermore, this paper shows that our benchmark algorithm is valuable when the measurement noise is large.

• Journal of Institute of Control, Robotics and Systems
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• v.12 no.1
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• pp.15-22
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• 2006

This paper presents an efficient localization scheme for an indoor mobile robot using RFID tags on the floor. The mobile robot carries an RFID reader at the bottom, which reads the RFID tags on the floor to localize the mobile robot. Each RFID tar on the floor stores its own absolute position which is used to calculate the position and velocity of the mobile robot. Locating the RFID tags on the floor, which constructs an intelligent sensor space, may require several factors to be considered: economics feasibility and accuracy. In this paper, the optimal allocation scheme of the RFID tags on the floor to satisfy the accuracy constraint has been proposed and verified by the experiments. Based on the RFID reading, the mobile robot navigation has been successfully demonstrated to avoid obstacles and to reach the goal within a pre-specified time.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2010.11a
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• pp.33-34
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• 2010

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.10a
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• pp.648-650
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• 2015

In this research, we investigate an influence of a reader antenna location in Passive UHF RFID system based Object localization. When the localization system uses stationary RFID reader system, the performances of the system are significantly varied depending on the deployed antenna conditions due to its external environment such as reflection by the ground and obstacles. In this research we deeply study the RF conditions and differentiation with various antenna location. According to the empirical results, the localization system shows the best performance, where the reader antenna locates 1.5m from the target area of interest.

• Proceedings of the IEEK Conference
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• 2009.05a
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• pp.103-105
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• 2009

This paper presents a pseudorandom tag arrangement for improved RFID based mobile robot localization. First, four repetitive tag arrangements, including square, parallelogram, tilted square, and equilateral triangle, are examined. For each tag arrangement, the difficulty in tag installation and the problem of tag invisibility are discussed. Then, taking into account both tag invisibility and tag installation, a pseudorandom tag arrangement is proposed, which is inspired from a Sudoku puzzle. It is shown that the proposed tag arrangement exhibits spatial randomness quite successively without increased difficulty in installation.