• Transactions of the Korean Society of Mechanical Engineers A
• /
• v.34 no.2
• /
• pp.183-189
• /
• 2010

A localization system for indoor robots is an important technology for robot navigation in a building. Our localization system imports the GPS system and consists of more than 3 satellite beacons and a receiver. Each beacon emits both an ultrasonic wave and radio frequency. The receiver in the robot computes the distance from it to the beacon by measuring the flying time difference between ultrasonic wave and radio frequency. It then computes its position with the distance information from more than 3 beacons whose positions are known. However, the distance information includes errors caused from the ultrasonic sensors; we found it to be limited to within one period of a wave (${\pm}2\;cm$ tolerance). This paper presents a method for predicting the maximum position error due to distance information errors by using Taylor expansion and singular value decomposition (SVD). The paper also proposes a measuring parameter such as sensitivity to represent the accuracy of the indoor robot localization system in determining the robot's position with regards to the distance error.

• The Journal of the Korea institute of electronic communication sciences
• /
• v.7 no.2
• /
• pp.391-399
• /
• 2012

This paper proposes a particle filter approach for SLAM(Simultaneous Localization and Mapping) of a mobile robot. The SLAM denotes estimation of both the robot location and map while the robot navigates in an unknown environment without map. The proposed method estimates robot location simultaneously with the locations of the ultrasonic beacons which constitute landmarks for navigation. The particle filter method represents the locations of the robot and landmarks in probabilistic manner by the distribution of particles. The method takes care of the uncertainty of the landmarks' location as well as that of the robot motion. Therefore, the locations of the landmarks are updated including uncertainty at every sampling time. Performance of the proposed method is verified through simulation and experiments. The method yields practically useful mapping information even if the range data from the landmarks include random noise. Also, it provides more accurate and robust estimation of the robot location than the usual least squares methods or dead-reckoning method.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.40 no.7
• /
• pp.1307-1312
• /
• 2015

The recent adoption of Bluetooth LE technology in smart phones triggered commercial interest in RSSI-based positioning technology. Estimation error in RSSI measurement due to the antenna pattern, multipath fading, environmental noise has to be considered for designing beacon systems. The paper proposes an analysis method and beacon planning rules for a small and isolated indoor service area, based on probabilistic model of RSSI estimation error. As an practically important guide, the beacons have to be installed at the boundary of the service area to minimize the maximum position error, whereas the beacons have to be evenly distributed in the service space to minimize the average estimation error.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.37 no.4B
• /
• pp.300-308
• /
• 2012

Many indoor location tracking technologies have been proposed. Generally indoor location tracking using TOA signal is used, there is a weak point that it's difficult to track the location due to obstacles like a refraction, reflection and dispersion of radio wave. In this paper, we apply ETOA(Estimated-TOA) algorithm in NLOS(Non-Line-Of-Sight) environment to solve above problem. In NLOS environment, TOA value between Beacon and Mobile node is predicted by ETOA algorithm and the tracking of indoor location is also possible to identify using two NLOS beacons of three beacons by this algorithm. We show that the proposed algorithm is accurate location tracking is accomplished using the applying the proposed algorithm to indoor moving robot and the inertia sensor of robot and Kalman filter algorithm.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.35 no.1B
• /
• pp.68-77
• /
• 2010

Wireless sensor networks are installed in various environments and collect sensing data through wireless links. The quality of a wireless link may be unstable due to environment causes and hardware performance in wireless sensor networks. Since the change of the link quality may cause data loss, sensor nodes need to adaptively estimate the change of the link quality. Also, the routing protocol should deal with this situation. In this paper, the adaptive link quality estimation and routing scheme in the large-scale wireless sensor networks are proposed. When the quality of a link is unstable, sensor nodes agilely estimate the quality of links, and the new route is selected. When quality of a link is stable, the link quality is occasionally estimated so that the energy consumption is reduced. Moreover, sensor nodes exchange less beacons in order to reduce an overhead in dense networks. In the case of sparse network, the sensor nodes exchange more beacons for finding a better route. We prove that the proposed scheme can improve the energy efficiency and reliability.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.39C no.9
• /
• pp.820-827
• /
• 2014

One of the basic problems in Wireless Sensor Networks (WSNs) is the localization of the sensor nodes based on the known location of numerous anchor nodes. WSNs generally consist of a large number of sensor nodes and recording the location of each sensor nodes becomes a difficult task. On the other hand, based on the application environment, the nodes may be subject to mobility and their location changes with time. Therefore, a scheme that will autonomously estimate or calculate the position of the sensor nodes is desirable. This paper presents an intelligent localization scheme, which is an artificial neural network (ANN) based localization scheme used to estimate the position of the unknown nodes. In the proposed method, three anchors nodes are used. The mobile or deployed sensor nodes request a beacon from the anchor nodes and utilizes the received signal strength indicator (RSSI) of the beacons received. The RSSI values vary depending on the distance between the mobile and the anchor nodes. The three RSSI values are used as the input to the ANN in order to estimate the location of the sensor nodes. A feed-forward artificial neural network with back propagation method for training has been employed. An average Euclidian distance error of 0.70 m has been achieved using a ANN having 3 inputs, two hidden layers, and two outputs (x and y coordinates of the position).

• Journal of the Korean Institute of Intelligent Systems
• /
• v.22 no.3
• /
• pp.353-360
• /
• 2012

This paper describes a SLAM method which estimates landmark locations and robot pose simultaneously. The particle filter can deal with nonlinearity of robot motion as well as the non Gaussian property of robot motion uncertainty and sensor error. The state to be estimated includes the locations of landmarks in addition to the robot pose. In the experiment, four beacons which transmit ultrasonic signal are used as landmarks. The robot receives the ultrasonic signals from the beacons and detects the distance to them. The method uses rang scanning sensor to build geometric feature of the environment. Since robot location and heading are estimated by the particle filter, the scanned range data can be converted to the geometric map. The performance of the method is compared with that of the deadreckoning and trilateration.

• Journal of the Korea Society of Computer and Information
• /
• v.24 no.11
• /
• pp.135-142
• /
• 2019

In this paper, we propose a system that can identify the position of naval ship personnel at a glance by utilizing the Bluetooth-based beacons. The system proposed in this paper, installs a beacon receiver which are short-range wireless communication devices for each cabin, and in the installed beacon receiver receives information from beacons held by personnel. The received information is transmitted to the processing server, and the processing server transmits the integrated information of the cabin to the display module. Display module displays personnel information located in each cabin. As a result of simulations using the designed system, it was confirmed that the integrated information is transferred to the display module and displayed. Unlike existing situations where personnel positions are reported orally within the ship, the system can quickly and in real time determine the position of personnel, allowing for the management of personnel in non-combat situations and the rapid battle disposition in combat situations. This is expected to contribute greatly to the improvement of fighting power.

• The Journal of The Korea Institute of Intelligent Transport Systems
• /
• v.19 no.6
• /
• pp.192-207
• /
• 2020

In industrial fields like big factories, efficient management of resources is critical in terms of time and expense. So, inefficient management of resources leads to additional costs. Nevertheless, in many cases, there is no proper system to manage resources. This study proposes a system to manage and track large-scale resources efficiently. We attached Bluetooth 5.0-based beacons to our target resources to track them in real time, and by saving their transportation data we can understand flows of resources. Also, we applied a diagonal survey method to estimate the location of beacons so we are able to build an efficient and accurate system. As a result, We achieve 47% more accurate results than traditional trilateration method.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.26 no.6
• /
• pp.936-942
• /
• 2022

While indoor vehicle navigation is an essential component in large-scale parking garages of major cities, technical limitations and challenging propagation environments considerably degrade the accuracy of existing localization techniques. This paper proposes a proximity detection scheme using low-cost beacons where a handheld mobile device within a moving vehicle autonomously detects its approximate position and moving direction by only observing Received Signal Strength (RSS) values of beacon signals. The proposed approach essentially exploits the differential RSS technique of multi-directional beams to reduce the impact of the environment, vehicle, and mobile device. A low-cost multi-directional beacon prototype is developed using Bluetooth technology. The localization performance is evaluated using 96 beacons in an underground parking garage within an area of 394.8m×304.3m. Experimental results show that the 90th percentile of the average proximity detection error is 0.8m. Furthermore, our proposed scheme provides robust proximity detection performance with various vehicles and mobile devices.