DEVELOPMENT OF RFID BASED 2D LOCALIZATION SIMULATION FOR AUTONOMOUS GUIDED VEHICLE ... · 2018-08-31 · Mohd Rodzi, Development of RFID Based 2d Localization Simulation for Autonomous

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International Journal of Mechanical Engineering and Technology (IJMET)

Volume 9, Issue 8, August 2018, pp. 496–507, Article ID: IJMET_09_08_054

Available online at http://www.iaeme.com/ijmet/issues.asp?JType=IJMET&VType=9&IType=8

ISSN Print: 0976-6340 and ISSN Online: 0976-6359

© IAEME Publication Scopus Indexed

DEVELOPMENT OF RFID BASED 2D

LOCALIZATION SIMULATION FOR

AUTONOMOUS GUIDED VEHICLE TRACKING

IN INDOOR ENVIRONMENT

Muataz Hazza Faizi Al Hazza*, Nur Izzati Zainal and Mohd Zuhaili Mohd Rodzi

Manufacturing and Materials Engineering Department, Faculty of Engineering,

International Islamic University Malaysia, Kuala Lumpur, Selangor, Malaysia

*Corresponding Author

ABSTRACT

The fast pace of technological development urges manufacturers to upgrade their

systems to compete with the current industry. Autonomous Guided Vehicle (AGV) is

one of the recent trends in the manufacturing. It promotes high efficiency and reduces

labor cost to the manufacturer. As an enhancement feature for the AGV, in this paper,

simulation of 2D localization for tracking an autonomous guided vehicle (AGV) using

RFID in an indoor manufacturing environment is presented. The localization

techniques are based on measuring the distance using path loss model from RSSI

values provided by RFID and coordinates calculation using trilateration algorithm

with multiple reference points. The mathematical process is coded in a software to

produce a graphical user interface (GUI) based simulation for 2D localization of AGV

tracking in indoor environment. Based from the data collected, the results show that

the distance is computed experimentally and theoretically resulted average errors of

4.00 % and the AGV locations is measured from the calculated distance using

trilateration algorithm. Therefore, the simulation can be enhanced for real time 2D

localization of AGV tracking in indoor environment.

Keywords: Localization algorithm, RSSI, trilateration and AGV.

Cite this Article: Muataz Hazza Faizi Al Hazza, Nur Izzati Zainal and Mohd Zuhaili

Mohd Rodzi, Development of RFID Based 2d Localization Simulation for

Autonomous Guided Vehicle Tracking in Indoor Environment, International Journal

of Mechanical Engineering and Technology, 9(8), 2018, pp. 496–507.

http://www.iaeme.com/IJMET/issues.asp?JType=IJMET&VType=9&IType=8

1. INTRODUCTION

The introduction of robots into manufacturing industry gives a huge impression toward

industrial players. In fact, it has galvanized study among researchers and inventors to develop

more prototypes that can contribute to the industry of making. Nowadays, a myriad of

RFID Based 2d Localization Simulation for Autonomous Guided Vehicle Tracking in Indoor

Environment


automation technologies was designed to bring ease to manufacturing systems. One of the

current trends in factory automation is the implementation of autonomous guided vehicle

(AGV). Although the installation of AGV in the factory requires high initial investments,

considering high labor cost that becomes the major challenge for industries, AGV may

possibly assist the manufacturer to solve the problem. Moreover, manufacturers are normally

aim for tremendous productivity with minimal operational cost. According to market research

report prepared by Grand View Research in 2016, the Asia Pacific regional market is assumed

to experience a huge growth which has engendered an increasing demand for AGV in their

industry [1]. Since AGV promotes solution to the existing issues, its implementation inspires

developers to enhance more upgradation features in it.

Developing an AGV in a flexible factory system is incomplete without knowing its

current location. A real time AGV location provide crucial information for the factory

workers to identify unpredicted circumstances that may happen during production process as

well as to monitor current AGV status. Since the past few years, numerous researches have

been done to explore algorithms that are best suited for localization. Localization algorithm

that is simpler such as trilateration and triangulation offer more flexibility for the developer to

practically implement it in the AGV with predefined coordinates. Although sometimes, a

complex algorithm provides better outcomes, it requires developers to design a device that

can solve its complexity and it might be costly since they need high performance equipment.

Therefore, in this research, trilateration-based localization algorithm is proposed for AGV

tracking in indoor environment.

In identifying the location of AGV, one of the prerequisite parameter that need to be

measured is distance. There are various devices introduced in the market for distance

measurement such as radio frequency identification (RFID), Zigbee and laser. These devices

usually provide received signal strength indication (RSSI) value which assists in distance

measurement. However, for cost reduction, developers opt to choose RFID since its

implementation offers more flexibility. In addition, Market Research Future (MRF) in 2017

had reported that RFID market is growing rapidly over approximately 15.76% of Compound

Annual Growth Rate (CAGR). The CAGR is predicted to reach at nearly USD$ 31.8 billion

by the end of forecast period which is in 2023 as depicted in Figure 1 [2]. The upsurge in

CAGP percentage indicates that RFID technology and demand will kept on blooming over the

year. Therefore, selecting RFID based technology for localization provides more convention

community in the world.

Figure 1 Radio-Frequency Identification (RFID) Market Report [2]

The fusion of RSSI based distance measurement which is obtained using RFID device and

triangulation for localization algorithm gives alternatives to the developer in the market on

how they want to design their system in the factory. Therefore, this research discussed the

Muataz Hazza Faizi Al Hazza, Nur Izzati Zainal and Mohd Zuhaili Mohd Rodzi


development of 2D localization simulation for AGV tracking in indoor manufacturing

environment as one of the potential solution as well as providing ideas and suggestion to the

researchers pertaining to the study on localization. In Section 2, some explications of related

works relevant to the topic of this research are discussed. Section 3 elucidates some details

explanation of the methodology involved in this study while Section 4 discussed the outcome

of the research based on the data collected and simulation performance. Finally, Section 5

briefly maps the research into a compact form of narration as a conclusion of the research.

2. RELATED WORKS

Researches on localization require a depth understanding in wireless transmission system in

indoor environment. The communication between RFID reader and tag is done through a

cordless medium. Therefore, there are several factors that could affect the reading process like

multipath propagation. However, RFID reader detection distance varies in different

frequency. Longer reading detection distance will be more affected by multipath propagation

compared to shorter distance. Therefore, this section elaborates some basic knowledge on

RFID, selected RFID operating frequency, RSSI based distance measurement and previous

work on localization algorithm.

2.1. UHF RFID for Localization System

RFID can be categorized into different operating frequency. Each range offer unique

functions suitable to the application involves in the implementation. Basically, there are three

basic ranges which are low frequency (LF, 30-300 kHz), high frequency (HF, 3-30 MHz), and

ultra-high frequency (UHF, 300-3 GHz) [3]. Figure 2 illustrates the frequency range of each

class in electromagnetic spectrum whereas Table I describes the RFID operating frequencies

and their respective passive reading distance.

Figure 2 RFID Frequency Range in Electromagnetic Spectrum [4]

Table I RFID Operating Frequencies [5]

Frequency Range Frequencies Passive Read

Distance

Low Frequency (LF) 120-140 kHz 10-20 cm

High Frequency (HF) 3-30 MHz 10-20 cm

Ultra High Frequency (UHF) 869-928 MHz 3m

This research requires tracking an AGV in indoor environment which needed reading

distance within few metres range. Among the three operating frequency classes of RFID,

UHF RFID is the best selection to be used in this research application. Therefore, UHF RFID

operating frequency is proposed in the hardware implementation.


Environment


2.2. Distance Measurement based on Received Signal Strength Indicator (RSSI)

using Friis Propagation Law

The received signal strength indicator (RSSI) referred to the measurement of power level

from RFID tag which is detected by the UHF RFID reader. There are some previous works on

UHF RFID that explain the usage of RSSI as a parameter to measure the distance between the

tag and the reader. Luh et al. (2013) had conducted a research on the measurement of

effective reading distance of UHF RFID passive tags using RSSI. The research elaborated the

theoretical background which involve the calculation of transmitted and received power in

wireless transmission system [6].

In another research, Dao et al. (2014) had discussed a study on an indoor localization

system using passive RFID. The study applied basic concepts of wireless transmission system

which is based on free space Radio Wave Propagation (RWP) [7]. Free space RWP

measurement is closely related with Friis transmission equation as shown in equation (1)

where in this case, PTag is power received by the RFID tag, PReader is power transmitted by the

reader, GReader is the gain of the UHF RFID antenna, GTag is the gain of the tag and PL is the

path loss [6] (Note that all the parameters are in dBm).

LaderTagaderTag PGGPP ReRe 1

Theoretically, the ideal equation of path loss, PL is shown in equation (2) where d is

referred to distance between the RFID tag and reader and λ is the wavelength of the signal [6].

(Note that the path loss is in dB).

dPL

4log20

2

However, due to various environment designs, path loss will be affected by many factors

[8]. Therefore, the path loss calculation is usually associated with another parameter which is

losses that occur due to the building interference (denoted as PL0). Thus, the model has

changed into an approximately linear log-distance form which is shown in equation (3) where

PL0 is the losses measured in the building and n is the named Ordinary Least Squares (OSL).

dnPP LL log200 3

In previous work done by Lau et al. (2007) in which the research elaborated more on RSSI

based distance estimation using equation (4) where RSSI is the RSSI in dBm, n is the path loss

exponent, d is distance between transmitter and receiver and A is RSSI value at 1meter

distance [9]. Therefore, by using reference distance equivalent to 1 metre, RSSI value can be

calibrated from experimental result. Thus, in this research, equation (4) may be used as

reference for measuring the distance.

)log10( 10 AdnRSSI 4

2.3. Related Works on Localization Algorithm

Over the past years, researchers have been actively studied on indoor algorithm using RFID

due to some demands such as security, safety and service. A lot of techniques proposed by

researchers for RFID based localization application in which the approaches are categorized

into three algorithms; distance estimation, scene analysis and proximity [10] [11].

Table II shows the list of RFIDs based localization algorithms and its class.



Table II Localization Algorithms and Its Class [11]

Class Algorithm (example)

Distance Estimation

Received Signal Strength

Time of Arrival

Time Difference of Arrival

Phase of Arrival

Angle of Arrival

Scene Analysis K Nearest Neighbor

Probabilistic Approach

Proximity -

Stefan et al. (2016) had performed a study on fingerprinting based 2D localization

algorithm using passive UHF RFID. The study proposed using outputs form multiple signal

classification (MUSIC) and Bartlett beamformer as fingerprint algorithm. The results are

evaluated using four different classification methods which are k Nearest Neighbour (kNN),

J48, Random Forest and Multi Perception Layer (MLP) algorithm [12]. Although the outcome

of the research proven to have the capability in localizing RFID tag within a segment of 0.23

m2 in 97% of the cases, the research approach requires in depth analysis since different places

need new observations. Besides that, the calibration requires a myriad of reference points

which are 1500-2000 points. Therefore, the methods are tough to be installed in a flexible

manufacturing environment.

In another research, Marton et al. (2016) had proposed a study on a robust trilateration

based indoor localization algorithm for omnidirectional mobile robots [13]. The distance is

measured based on time of flight of the signals travelling between the transmitters and

receivers. Then, localization algorithm is furthered calculated using the distance values. This

approach suggests sensor fusion method for fast localization by using measured information

provided by Inertial Measurement Unit (IMU) implemented on the robot. The results show

that mobile robots can be tracked using trilateration algorithm with accurately. However, in a

real-time application, the implementation is usually associated with some unpredicted but

acceptable errors.

2.4. Comparison of Previous Works

The fundamental of this research is focusing on development of RFID based 2D localization

simulation for AGV tracking in indoor environment. Based on the summary of previous

works listed in Table III, inaccurate distance measurement contributes major error in

localization calculation. Although Luh et al. and Dao et al. performed almost similar approach

for measuring distance, this approach is ideal for short distance range measurement [14].

Meanwhile, Stefan et al. use a complex approach of MUSIC algorithm which makes it harder

to physically mount the systems in real world scenario due to its complexity [15]. Besides

that, Marton et al. use IMU for measuring distance which is rarely used in localization

algorithm for autonomous mobile robot.


Environment


Table III Table of Comparison

Author (Year)

Algorithm Approach Limitation

Luh et al. (2013)

RSSI based distance measurement using Friis

transmission formula.

The usage of Friis transmission formula in real life incorporated with unpredictable signal loss which

engenders inaccuracy in the measurement.

Dao et al. (2014)

Distance estimation usng RWP model and kNN

based localization algorithm.

RWP model use similar concept of Friis transmission law. The result is associated with unexpected error due

to multipath propagation from surrounding.

Stefan et al. (2016)

Fingerprinting based localization algorithm

using MUSIC and Bartlett beamformer.

The calibration process requires a myriad of reference points which are 1500-2000 points. This might be due

to small distance detection range of RFID. Besides that, MUSIC algorithm is quite complex to achieve.

Marton et al. (2016)

Time of flight based distance measurement and

trilateration based localization algorithm.

There are a number of complicated process used to identify the location of mobile robots. In addition, IMU

is rarely used in localization application.

2.5. Open Issue

Despite of the contemporary research work discussed, majority of the localization algorithm

are simulated into several fractions. None of the researches design their own simulation

interface to solidify the idea of practically implement the AGV localization system in real

world scenario such as in indoor manufacturing environment. Therefore, in this research,

simulation of 2D localization using RFID for AGV tracking in indoor manufacturing

environment is proposed to provide optional solution to the developer and researcher.

3. METHODOLOGY

Planning an AGV system in a real working environment is a challenging task to the engineers.

Usually, an AGV is mainly programmed to deliver things to the target location. During the

delivery process, the person in charge must observe the AGV’s performance in term of

location, safety, speed, operation and a number of other things. One of the aim of this research

is to simulate the AGV location in real-time. This research proposed a trilateration-based

algorithm using RSSI value measured from RFID reader which is implemented in the AGV

system. The development of the proposed system required an accurate and stable data reading

within a desired distance. The methodology of the system implementation is illustrated in

Figure 3 which is began with path loss model, trilateration technique, predefined AGV

pathway and GUI application development.

Figure 3 Methodology of the System Implementation



3.1. Path Loss Model based on RSSI for Distance Estimation

The designation of path loss model requires data collection from the experiment. For

simplicity, the RSSI and distance, d abbreviations in Equation (4) are substituted with yrssi and

xd generating the following equation (equation (5)) [16]:

Axny drssi 10log10 5

The path loss exponent, n is approximated to 2.5 (median value of n inside a factory with

no line of sight), based on Table IV which is taken from the recorded data by Shahin in 2008

[16]. The value of n may be varied depend on the percentage error of the results.

Table IV Path Loss Exponent (n) for Different Environments [17]

n Environment

2.00 Free space

1.60 – 1.80 Inside a building, line of sight

1.80 Grocery store

1.80 Paper/cereal factory building

2.09 A typical 15m x 7.6m conference room with table and chair

2.20 Retail store

2.00 – 3.00 Inside a factory, no line of sight

2.80 Indoor residential

2.70 -4.30 Inside a typical office building, no line of sight

By referring the RSSI value from experimentation result which are taken within range of

0.5 meter to 1.5 meter from the UHF RFID, unknown value of A from Equation 5 is

calculated, resulting equation (6).

59log25 10 drssi xy 6

Using equation (5), By referring the RSSI value from experimentation result which are

taken within range of 1.5 meter to 3.0 meter from the UHF RFID, unknown value of A from

Equation 5 is calculated, resulting equation (6). The average percentage error for equation (6)

is 6.3025%. However, to optimize the accuracy level and reduce percentage of error, the n

parameter is varied as shown in Table V. Based on the result depicted below, the value of n is

reselected with lowest percentage of error.

Table V Path Loss Exponent, n and Its Percentage of Error

Path loss exponent, n Percentage of Error (%)

2.0 3.84522

2.1 4.04724

2.2 4.25031

2.3 4.45446

2.4 4.65970

2.5 4.87765

2.6 5.09890

2.7 5.32129

2.8 5.54483

2.9 5.76955

3.0 5.99546

Table V shows that the lowest percentage of error occur when n is equivalent to 2.0.

Therefore, the equation is improved by replacing n = 2.0, resulting equation (7) and the new

average percentage of error measured is 4.91371 %. Figure 4 illustrated the resulted graph of

RSSI versus distance.


Environment


Figure 4 Graph of RSSI (dBm) versus Distance (m)

3.2. Trilateration Technique to Compute AGV Coordinate in x and y-axis

In trilateration algorithm, distance will be the main parameter to solve the equation. This

value is already computed in section 3.1. Therefore, assume that distance between the AGV

and the reference point, Ri is denoted as di where i = 1,2,3 … N, the equation is shown in

equation (7).

222222

222

22 iiiiii

iii

yyyyyxxxxd

yyxxd

7

To simplify the calculation, equation (7) which is non-linear is subtracted with dN2 and di

2

resulting equation (8) which is linear.

2222222222 NNNNiiiiNi yyyxxxyyyxxxdd 8

Unknown value of x and y in equation (8) can be solved using matrix form as depicted in

equation (10). Meanwhile, b and A are represented in equation (11) and (12).

yyyxxxyxdyxd NiNiNNNiii 22222222

9

y

xAb

10

2222

1

2

1

2

1

2222

3

2

2

2

2

2222

1

2

1

2

1

NNNNNN

NNN

NNN

yxdyxd

yxdyxd

yxdyxd

b

11

NNNN

NN

NN

yyxx

yyxx

yyxx

A

11

22

11

2

12

Trilateration algorithm requires 3 reference points (N=3) so that equation (10) can be

solved when matrix A is inversed.



3.3. Predefined AGV Track in Cartesian Coordinate with Multiple Reference

Point

Before AGV’s coordinate is computed, AGV track is planned and sketched in a cartesian

coordinate system. Assume that the AGV pathway will be installed in indoor manufacturing

environment, the pathway is designed in a circulation layout with scale of 1:1 meter as

illustrated in Error! Reference source not found.. In the predefined AGV track, 15 reference

points labels as R1-R15 are required to ensure AGV’s coordinate computation is possible.

Furthermore, for every point along the AGV track, there should be exactly 3 reference points

detected. Therefore, by assuming the maximum distance for the RFID reader to record a

stable reading is 3 meters, based on all rules stated, the reference points are positioned as

shown in Error! Reference source not found.

Figure 5 Predefined AGV Pathway Layout in Cartesian Coordinate

3.4. GUI Development for 2D Localization Simulation for AGV Tracking

The process is continued with development of GUI. The idea of introducing GUI in this

research is to visually locate the AGV in real time scenario. In this section, the function for

each button in the application is elaborated.

Figure 6 2D AGV Tracking Simulation Application


Environment


Figure 6 illustrate the menu page of the application. This application consists of seven

button which function are listed in Table VI

Table VI GUI Buttons and Its Function

Button Function

Connect To establish connection between the device and the computer.

Disconnect To terminate connection between the device and the computer.

Read

To initiate reading process of the system. Once data is read, information such as RSSI value and tag ID will be displayed and automatically calculated. Meanwhile, the measured location is visually updated in the AGV layout shown in the application.

Pause To hold the reading process of the system.

Stop To stop the reading process of the system.

Save To save the recorded data by the application (RSSI, tag ID,

distance and location).

Info To show some introduction about the application.

4. RESULTS AND DISCUSSIONS

Further analysis on the experimentation results are elucidated in this section. Besides that, the

GUI application performance are also evaluated and elaborated. The discussion is started with

path loss model for estimating the distance based on RSSI and evaluation of developed

software for 2D localization for AGV tracking.

4.1. GUI Development for 2D Localization Simulation for AGV Tracking

In distance estimation, where RSSI value are collected for generating path loss model, the

actual and experimentation value of RSSI are recorded with distance ranging from 0.5 meter

to 1.5 meter. The RFID reader is programmed to operate at 20 dBm resulting the data as listed

in Table VII Based on calculated percentage error, the overall average error obtained is 4.00

% which is considered low and reasonable.

Table VII Distance, RSSI and Its Percentage of Error

Distance (m)

RSSI (dBm) (experiment)

RSSI (dBm) (theoretical)

Percentage of Error (%)

0.5 -54 -52.98 1.93

0.6 -56 -54.56 2.63

0.7 -59 -55.90 5.54

0.8 -60 -57.06 5.15

0.9 -62 -58.08 6.74

1.0 -62 -59.00 5.08

1.1 -60 -59.83 0.29

1.2 -58 -60.58 4.26

1.3 -59 -61.28 3.72

1.4 -60 -61.92 3.10

1.5 -59 -62.52 5.63

Figure 7 illustrate the difference of RSSI value in experimentation (labelled with blue dot)

and its theoretical value to graphically show the distinction between them. The logarithmic

line shown in Figure 7 represent equation (7) as measured in Section 3.



Figure 7 Graph of RSSI (dBm) versus Distance (m) (Experimentation and Theoretical)

4.2. Evaluation of Developed Software for 2D Localization for AGV Tracking

Simple approach of trilateration algorithm makes the programming process easier and faster.

Therefore, the AGV tracking can be done in a real time considering fast process computation

performed by the application. By using the GUI application, the data collected from the

application can be recorded and observed. The x and y coordinate are determined based on the

nearest AGV pathway coordinate. Based on the computed coordinate, AGV location is

automatically updated in the simulation as shown in Figure 8 for example.

Figure 8 Screenshot of 2D Localization Simulation for AGV Tracking

5. CONCLUSIONS

This research had produced positive outcomes in providing solution for AGV localization

implemented in the factory. The results show that the designed application can possibly be

installed and used in the manufacturing system. In relevance to the issue faced by developer

who want another alternative for localization algorithm for autonomous mobile robot, this

research had successfully proposed trilateration-based approach for computing the

autonomous mobile robot in predefined pathway. Therefore, the main objective of this

research which is to develop 2D localization simulation for AGV tracking in indoor

environment is successfully achieved.


Environment


ACKNOWLEDGEMENT

The authors would like to express their sincere gratitude to the Research Management Centre,

International Islamic University Malaysia and the Ministry of Science, Technology and

Innovation (MOSTI). This research was sponsored and published under the project SF15-017-

0067. This work was funded by e-science fund from MOSTI.

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Documents

DEVELOPMENT OF RFID BASED 2D LOCALIZATION SIMULATION FOR AUTONOMOUS GUIDED VEHICLE ... · 2018-08-31 · Mohd Rodzi, Development of RFID Based 2d Localization Simulation for Autonomous