• International Journal of Fuzzy Logic and Intelligent Systems
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• v.14 no.1
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• pp.41-48
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• 2014

This paper analyzes the Monte Carlo localization (MCL) method, which estimates the pose of an indoor mobile robot. A mobile robot must know where it is to navigate in an indoor environment. The MCL technique is one of the most influential and popular techniques for estimation of robot position and orientation using a particle filter. For the analysis, we perform experiments in an indoor environment with a differential drive robot and ultrasonic range sensor system. The analysis uses MATLAB for implementation of the MCL and investigates the effects of the control parameters on the MCL performance. The control parameters are the uncertainty of the motion model of the mobile robot and the noise level of the measurement model of the range sensor.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.63 no.2
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• pp.277-283
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• 2014

This paper proposes robot localization using Fuzzy-Extended Kalman Filter algorithm of the mobile robots equipped with least sensors. In order to improve the accuracy of the localization, we usually add the sensors or equipment. However, it increases the simulation time and expenses. This paper solves this problem using only the odometer and ultrasonic sensors to get the localization with the Fuzzy-Extended Kalman Filter algorithm method. By inputting the robot's angular velocity, sensor data variation, and residual errors into the fuzzy algorithm, we get the sensor weight factor to decide the sensor's importance. The performance of the designed method shows by the simulation and Pioneer 3-DX mobile robot test in the indoor environment.

• 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.

• 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.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.920-923
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• 1996

This paper presents a new algorithm for the self-localization of a mobile robot using 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 the 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 parallel walls 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(V.P). Point features for computing cross ratios are extracted robustly using a vanishing point and the intersection points between floor and the vertical lines of door frames. The robustness and feasibility of our algorithms have been demonstrated through experiments in indoor environments using an indoor mobile robot, KASIRI-II(KAist SImple Roving Intelligence).

• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.37.1-37
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• 2002

Localization is one of the most important functions for the mobile robot navigating in the unstructured environment. Most of previous localization schemes estimate current position and pose of mobile robot by applying various localization algorithms with the information obtained from sensors which are set on the mobile robot, or by recognizing an artificial landmark attached on the wall, or objects of the environment as natural landmark in the indoor environment. Several drawbacks about them have been brought up. 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 sch...

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.5
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• pp.1339-1355
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• 2023

Localization is a hot research spot for many areas, especially in the mobile robot field. Due to the weak signal of the global positioning system (GPS), the alternative schemes in an indoor environment include wireless signal transmitting and receiving solutions, laser rangefinder to build a map followed by a re-localization stage and visual positioning methods, etc. Among all wireless signal positioning techniques, Wi-Fi is the most common one. Wi-Fi access points are installed in most indoor areas of human activities, and smart devices equipped with Wi-Fi modules can be seen everywhere. However, the localization of a mobile robot using a Wi-Fi scheme usually lacks orientation information. Besides, the distance error is large because of indoor signal interference. Another research direction that mainly refers to laser sensors is to actively detect the environment and achieve positioning. An occupancy grid map is built by using the simultaneous localization and mapping (SLAM) method when the mobile robot enters the indoor environment for the first time. When the robot enters the environment again, it can localize itself according to the known map. Nevertheless, this scheme only works effectively based on the prerequisite that those areas have salient geometrical features. If the areas have similar scanning structures, such as a long corridor or similar rooms, the traditional methods always fail. To address the weakness of the above two methods, this work proposes a coarse-to-fine paradigm and an improved localization algorithm that utilizes Wi-Fi to assist the robot localization in a geometrically similar environment. Firstly, a grid map is built by using laser SLAM. Secondly, a fingerprint database is built in the offline phase. Then, the RSSI values are achieved in the localization stage to get a coarse localization. Finally, an improved particle filter method based on the Wi-Fi signal values is proposed to realize a fine localization. Experimental results show that our approach is effective and robust for both global localization and the kidnapped robot problem. The localization success rate reaches 97.33%, while the traditional method always fails.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.6 no.2
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• pp.161-166
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• 2006

Localization of mobile agent within a sensing network is a fundamental requirement for many applications, using networked navigating systems such as the sonar-sensing system or the visual-sensing system. To fully utilize the strengths of both the sonar and visual sensing systems, This paper describes a networked sensor-based navigation method in an indoor environment for an autonomous mobile robot which can navigate and avoid obstacle. In this method, the self-localization of the robot is done with a model-based vision system using networked sensors, and nonstop navigation is realized by a Kalman filter-based STSF(Space and Time Sensor Fusion) method. Stationary obstacles and moving obstacles are avoided with networked sensor data such as CCD camera and sonar ring. We will report on experiments in a hallway using the Pioneer-DX robot. In addition to that, the localization has inevitable uncertainties in the features and in the robot position estimation. Kalman filter scheme is used for the estimation of the mobile robot localization. And Extensive experiments with a robot and a sensor network confirm the validity of the approach.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.20 no.2
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• pp.133-138
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• 2011

RFID system can be used to improve object recognition, map building and localization for robot area. A novel method of indoor navigation system for a mobile robot is proposed using RFID technology. The mobile robot With a RFID reader and antenna is able to find what obstacles are located where in circumstance and can build the map similar to indoor circumstance by combining RFID information and distance data obtained from sensors. Using the map obtained, the mobile robot can avoid obstacles and finally reach the desired goal by $A^*$ algorithm. 3D map which has the advantage of robot navigation and manipulation is able to be built using z dimension of products. The proposed robot navigation system is proved to apply for SLAM and path planning in unknown circumstance through numerous experiments.

• Journal of the Institute of Convergence Signal Processing
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• v.9 no.2
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• pp.164-172
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• 2008

This paper presents the efficient localization of a mobile robot traveling on the floor with tags installed, using the spatial and temporal information acquired from passive RFID environment. Compared to previous research, the proposed localization method can reduce the position estimation error and also cut down the initial cost tag installation cost. Basically, it is assumed that a mobile robot is traveling over a series of straight line segments, each at a certain constant velocity, and that the number of tags sensed by a mobile robot at each sampling instant is at most one. First, the velocity and position estimation of a mobile robot starting from a known position, which is valid for all segments except the first one. Second, for the first segment in which the starting position is unknown, the velocity and position estimation is made possible by enforcing a mobile robot to traverse at least two tags at a constant velocity with the steering angle unchanged. Third, through experiments using our passive RFID localization system, the validity and performance of the mobile robot localization proposed in this paper is demonstrated.