OWARDS A COMPUTATIONALLY EFFICIENT RELATIVE

POSITIONING SYSTEM FOR INDOOR ENVIRONMENTS

An RFID Approach

Md. Suruz Miah and Wail Gueaieb

School of Information Technology and Engineering, University of Ottawa

800 King Edward Avenue, Ottawa, Ontario, Canada

Keywords:

Relative positioning system, RFID, Received signal strength, Mobile robot navigation.

Abstract:

The recent advancements of Radio Frequency IDentiﬁcation (RFID)-based localization approach has necessi-

tates the development of effective solutions for mobile robot navigation systems in an indoor and/or outdoor

environment. Among the most common problems pertaining to the modern RFID-based robot navigation sys-

tems are that multiple reference RF stations or excessive number of sensors are utilized for the location sensing

with RFID, however, particularly in indoor environments, spatial layout or cost problems limit the applicability

of those approaches. The contribution of the current manuscript is to devise a simple computationally efﬁcient

relative positioning system for indoor environments through a modiﬁed RFID tag architecture. The validity of

the proposed RFID-based RPS is demonstrated using the real data collected in a typical indoor environment.

NOMENCLATURE

N Total number of RFID tags

ˆp Estimated robot position

p True robot position

Position of tag i

ˆe Robot position error

∆RSS Received signal strength difference

RSS

Average RSS value of tag i

RSS

RSS value of tag i

1 INTRODUCTION

Due to the advent of RFID and RFID systems (Nasri

et al., 2008), and their applications in the ﬁeld of

robotics (Milella et al., 2007), positioning systems

have been used to deliver location information in in-

door and/or outdoor environments. The primary role

of such localization systems is to estimate and re-

port geographical information pertaining to the data

processing unit associated with a mobile robot for

the purpose of management, enhancement, and per-

sonalization services. The current manuscript con-

tributes to the design and implementation of a mod-

ular, cost-effective, and an easy-to-implement mo-

bile robot navigation algorithm in cooperation with

an open RFID hardware architecture.

Most of the RFID-based navigation systems sug-

gested in the literature are tailored along with the lo-

calization systems where the central task of an RFID

system is to estimate the position of a mobile robot at

a certain time instant. In the current work, an RFID

reader is mounted on a mobile robot and some RFID

tags are placed at 3-D locations (ceiling, for exam-

ple) in an indoor environment. At every time instant,

the reader broadcasts a time-varying Radio Frequency

(RF) signal to all tags in its operating range and tags

simply response back to the reader with their Re-

ceived Signal Strength (RSS) measurements. These

RSS values are then used by the mobile robot to ap-

proximate its relative position with respect to a de-

sired path that the robot has to follow. Despite the

signiﬁcant contributions of RFID systems and RSS

measurements in the literature to date, the localiza-

tion problem of a mobile robot remains some sig-

niﬁcant technical challenges that must be overcome.

Hence, our effort is devoted to the development of a

positioning system for an indoor mobile robot where

the previous methods might not work. The main con-

tributions of the current work is to devise a com-

putationally efﬁcient relative positioning system for

indoor environments using a modiﬁed RFID tag ar-

chitecture. This approach is different from the ex-

isting RSS-based localization methods (Graefenstein

and Bouzouraa, 2008) in that it uses the RSS mea-

surements of the RF signal transmitted by the RFID

331

Miah M., Gueaieb W. and Gueaieb W. (2009).

TOWARDS A COMPUTATIONALLY EFFICIENT RELATIVE POSITIONING SYSTEM FOR INDOOR ENVIRONMENTS - An RFID Approach.

In Proceedings of the 6th International Conference on Informatics in Control, Automation and Robotics - Robotics and Automation, pages 331-336

DOI: 10.5220/0002224403310336

 SciTePress

reader. This is simply because the passive tag circuit

is energized from the RF signal broadcasted by the

reader. Hence, the RSS value in the tag circuitry is

more signiﬁcant than that in the RFID reader.

The rest of the paper is outlined as follows. Some

of the most commonly used robot navigation and/or

localization systems are given in section 2. Section 3

describes the proposed RFID-based relative position-

ing architecture followed by fundamentals of RFID

system theory and its limitations. The RFID system

implementation is discussed in section 4 followed by

some real-time experimental results illustrated in sec-

tion 5. Finally, conclusions with some future research

avenue are drawn in section 6.

2 RELATED WORK

Mobile robot navigation and/or localization system

has been the subject of several studies. Among the

most common and popular navigation algorithms sug-

gested in the state of the art are dead-reckoning-based,

landmark-based, vision-based, behavior-based navi-

gation techniques. Each of these navigation tech-

niques has its own advantages and disadvantages, al-

though it is difﬁcult to rate them objectively. How-

ever, some aspects can be unequivocally compared,

such as the computational complexity, the navigation

accuracy, or the amount of information a priori re-

quired for the proper operation of the algorithm.

The fundamental idea behind the dead-reckoning

navigation techniques is that they provide position,

heading, translational, and rotational velocities of

an autonomous mobile robot. These techniques are

widely used due to their simplicity and easy main-

tenance (D’Orazio et al., 1993). The shortcomings

of this technique is obviously that small precision er-

rors and sensor drifts inevitably lead to increasing

cumulative errors in the robot’s position and orien-

tation over time, unless an independent reference is

used periodically to correct the errors. As an alterna-

tive to dead-reckoning-based methods, natural or ar-

tiﬁcial landmarks have been used at various locations

in the environment in order to better estimate the posi-

tion of the mobile robot (Lin and Lal Tummala, 1997;

Yi and Choi, 2004). Nevertheless, a landmark-based

navigation strategy relies on identiﬁcation and sub-

sequent recognition of distinct features or objects in

the environment that may be a priori known or ex-

tracted dynamically. Due to sensors noise and pos-

sible dynamic changes of the operating environment,

the recognition process of features or objects might

become quite challenging. Given the shortcomings

of the landmark-based techniques, some researchers

shifted their interest to vision-based navigation sys-

tems. Vision sensors can have wide ﬁeld-of-view,

can have millisecond sampling rates, and can be eas-

ily used for trajectory planning (Desouza and Kak,

2002). Yet, some disadvantages of vision include lack

of depth information, image occlusion, low resolution

and the requirement for extensive data interpretation

(recognition). As the development of different au-

tonomous robot navigation techniques in real-world

environments constitutes one of the major trends in

current research on robotics, one important problem

is to cope with the large amount of uncertainty inher-

ited from natural environments. As such, soft com-

puting techniques have received a considerable at-

tention in recent years. Numerous navigation tech-

niques have been suggested in the state of the art us-

ing some tools of computational intelligence such as

fuzzy logic, neural network, neuro-fuzzy system, ge-

netic algorithm, or several combinations of them.

With these concerns in mind, several works have

considered localizing a mobile robot based on the ap-

plication of emerging RFID technology owing to its

wide availability, non-touch recognition system that

transmits and processes the information on events and

environments using a wireless frequency and small

chips. Since an RFID system can recognize at high-

speed and send data within various distances, the ap-

plication of RFID technology has been increased and

RFID systems have been applied for the robot tech-

nology recently (Kulyukin et al., 2004).

Hahnel et al. studied to improve the localization

with a pair of RFID antennas (Hahnel et al., 2004).

They presented a probabilistic measurement model

for RFID readers that allow them to accurately local-

ize the RFID tags in the environment.

In addition, robot’s position estimation techniques

can be classiﬁed as range-based and bearing-based.

The main idea behind range-based techniques is to tri-

laterate the robot’s position using some known refer-

ence points and the estimated distances at those points

in the environment. Distances can be estimated from

either RSS measurements or time-based methods. Al-

though a small subset of such works have explored the

use of Time of Flight (ToF) (Lanzisera et al., 2006)

or Time Difference of Arrival (TDoA) measurements,

RSS is generally the feature of choice for indoor posi-

tioning. This is due to the fact that RSS measurements

can be obtained relatively effortlessly and inexpen-

sively. In addition, no extra hardware (e.g., ultrasonic

or infra-red) is needed for network-centric localiza-

tion (Youssef, 2004). On the other hand, bearing-

based schemes use the direction of arrival (DoA) of

a target. However, these schemes require multiple

range sensors in order to be better suited for mobile

ICINCO 2009 - 6th International Conference on Informatics in Control, Automation and Robotics

332

robot applications (Kim and Chong, 2009).

3 PROPOSED RFID-BASED

RELATIVE POSITIONING

(RPS)

The fundamental problem in most real-world local-

ization systems is to produce position estimate from

past observations on a discrete grid of points in an en-

vironment. Despite the signiﬁcant limitations of RSS

measurements stated in the literature, the proposed

approach seeks a function modeled by

ˆp = f (p

, . . . , p

where N is the total number of RFID tags placed in a

3D workspace, p

= (x

, y

, z

) with 1 ≤ i ≤ N repre-

sents the coordinates of an RFID tag in the world co-

ordinate system, and f is a function of RSS measure-

ments associated with the RFID tags. ˆp = (x

, y

, z

)

is the estimated relative position of the robot with re-

spect to the desired path on the ground. In the cur-

rent work, the position estimation is restricted to the

2D space due its simplicity, as such, z

, which is the

height information, is simply ignored. To quantify the

navigation accuracy, the error model is deﬁned by

ˆe =

ˆp − p

where p is the true position of the mobile robot.

In order to compute the estimated relative position

of the mobile robot using an RFID system, RFID tags

are arranged in a ﬁxed pattern on the ceiling, for in-

stance, as depicted in Figure 1. An RFID reader is

mounted on the mobile robot and four tags are at-

tached to the ceiling. The points P1, P2, P3, and

P4 deﬁne the orthogonal projections of the four tags

on the ground. The robot is supposed to navigate

along the virtual desired path deﬁned by the projec-

tion points.

3.1 Technical Background of an RFID

System

We now review the fundamental properties of a com-

mercially available RFID system in the market. RFID

is a type of automatic object identiﬁcation system.

The principle of an RFID system consists of storing

an individual static binary code to every object that

need to be identiﬁed and the automatic seizing of in-

formation via radio waves. An RFID system is mainly

composed of three main components: a tag, an RFID

reader, and a host computer (Peris-Lopez et al., 2006).

Tag 2

Tag 1 Tag 3

Tag 4

Source tags

Destination tags

−Y

Ceiling

Ground

−X X

Mobile robot

Right of path

Left of path

In path

Figure 1: Relative position system setup.

The tag is composed of a microchip with some ba-

sic storage capabilities, and a coupling element such

as antenna coil for communication. An RFID reader

is generally composed of an RF module, a control

unit, and a coupling element to interrogate electronic

tags via RF communications. The purpose of the host

computer is to execute a special purpose software in

order to store, process, and analyze the data acquired

by the reader. In the current work, an RFID reader is

interfaced with the robot’s central processing unit to

perform further processing of tags’ information.

3.2 Relative Positioning Technique

As mentioned above, most of the existing RFID sys-

tems available in the market provide only static in-

formation which limit its applicability in many real-

world proximity-based RFID applications. In the cur-

rent work, we propose a navigation strategy for guid-

ing a mobile robot in an indoor environment using a

customized RFID tag architecture that allows to en-

code some dynamic information along with its exist-

ing static ID. Figure 2 depicts a customized model of

an RFID tag employed in the current research. The

tag receives an RF signal transmitted by the reader

which is then rectiﬁed to get its RSS value. In the

present RFID system, the tag has some processing

capability to convert the RSS value into an 8-bit bi-

nary code. As can be seen in ﬁgure 2, the RSS mea-

surement of the RFID reader query is embedded with

the tag’s existing static binary ID (16-bit in this case)

which is then backscattered to the RFID reader. It is

important to articulate the fact that the reader archi-

tecture of the proposed RFID system requires no cus-

tomization as it would read the 24-bit (16-bit tag-ID +

8-bit RSS) frame in exactly the same way it normally

reads tag-IDs. The RFID reader extracts the frame

backscattered by the tag which is then passed to the

TOWARDS A COMPUTATIONALLY EFFICIENT RELATIVE POSITIONING SYSTEM FOR INDOOR

ENVIRONMENTS - An RFID Approach

333

Communication

Other Devices

Memory

RFID tag

Tag’s customization

Electromagnetic wave

Energy

Wireless

DC Power Generator

Frame (24 bits)

16 bit

Tag ID

Received signal strength (dBm)

8 bit

RFIDrReader

Interface

Transceiver

Figure 2: Proposed RFID architecture.

processing element on the robot’s board to decode it

into a tag-ID and an RSS value. The RSS values are

used to approximate the relative position of the mo-

bile robot with respect to its desired path.

We now explain how the relative position of the

robot can be approximated by incorporating tags’

RSS values in an indoor environment. In this work,

the robot is presented with a set of four tag-IDs,

S = {1, 2, 3, 4}, for instance, where tags with IDs 1

and 2 deﬁne the source (starting) point, and the tags

with IDs 3 and 4 deﬁne the destination, respectively.

Note that the tag coordinates in the world coordinate

system are not necessarily known. The robot com-

putes its position with respect to the desired path by

extracting and decoding the frames backscattered by

fours tags deﬁning the path. The RSS values are then

used to model the relative position which is deﬁned

∆RSS = (RSS

+ RSS

) − (RSS

+ RSS

) , (1)

where the RSS

with 1 ≤ i ≤ 4 represents the average

RSS value associated with tag i. The RSS samples

received from each tag are passed to an M-point mov-

ing average ﬁlter for better estimation. The ﬁlter is

modeled as

RSS

∑

j=1

RSS

∀i ∈ S , (2)

where RSS

is the j

RSS value at tag i. The signif-

icance of ∆RSS is the amount of robot’s divergence

from its desired path. Ideally, ∆RSS is closest to nil

when the robot is on the right track. It diverges from

zero as the robot moves drifts away from its path. The

sign of ∆RSS then depends on the side of the path the

robot is located.

(a)

(b)

Figure 3: RFID system (a) RF module used to emulate an

RFID tag

and (b) Make controller board used to emulate

an RFID reader

4 RFID SYSTEM

IMPLEMENTATION

The proposed RFID system is emulated using the

XBee Pro Modules

shown in Figure 3(a) as an in-

tegrated RF solution. The modules include MC13193

RF chip by freescale, which is compliant to the IEEE

802.15.4 norm (Graefenstein and Bouzouraa, 2008).

One of the XBee Pro modules is attached to the Make

Controller (MC) board

(ﬁgure 3(b)) to emulate a

commercial RFID reader. The MC board secures the

communication between the emulated RFID reader

and the robot.

In order to obtain an RSS value from a tag i,

1 ≤ i ≤ 4, the reader broadcasts a message with its

own static address. The tags are simply conﬁgured to

reply to the reader’s query with their individual binary

frames. As mentioned above, each tag’s frame con-

sists of its 16-bit static address and 8-bit RSS value.

http://www.digi.com/products/wireless/point-multipoint/xbee-pro-

series1-module.jsp, http://www.digi.com

http://www.makingthings.com/, http://www.makingthings.com

ICINCO 2009 - 6th International Conference on Informatics in Control, Automation and Robotics

334

The reader simply extracts and decodes the frames in

order to get the tag’s ID and the corresponding RSS

value and then passes them along to the mobile robot

for further processing.

5 EXPERIMENTAL RESULTS

The purpose of this section is to provide details on the

experimental evaluation of the proposed relative posi-

tioning system using the emulated customized RFID

tag architecture. The performance is evaluated using

real data in a research laboratory that reﬂects a typical

indoor operating environment.

The experiments of the proposed RFID-based

RPS were carried out at discrete points distributed

over approximately 3 ×6 ×2 m test area of a research

laboratory with four tags attached on four different

posts (≈ 2 m high). The test area is divided into uni-

form square grids of 30 × 30 cm

. The layout of the

test environment is depicted in Figure 4. The orthog-

onal projection points of the four tags on the ground

are what we call herein S1 and S2 for source, and D1

and D2 for destination. The desired trajectory is the

line linking the midpoints of the lines connecting S1

and S2, and D1 and D2, respectively.

To test the proposed RPS concept, ∆RSS is com-

puted at 30 different locations: 10 on the desired robot

trajectory (shaded area in ﬁgure 4), 10 on its left, and

10 on its right. The results are revealed in Table 1.

Each RSS value (in dB) is the output of an 8-point

moving average ﬁlter as deﬁned in (2). It can be seen

from Table 1 that the data corresponding to the left of

the path is globally larger than zero, which conﬁrms

that the robot is indeed out of its desired trajectory.

However, the same conclusion cannot be generalized

on the data collected on the right of the path since it

is generally close to that collected on the path. This

may be due to several reasons. The side test locations

are only 0.6 m off the path, which is an insigniﬁcant

distance compared to the height of the emulated RFID

tags (2 m). In other words, the distance between S1

and robot location 5, for instance, is not signiﬁcantly

different from that between S1 and location 5R. This

is a main source of ambiguity which contributes to

this lack of precision. We believe that ∆RSS would

be more distinguishable across the three regions if the

side locations were at least 3 m off the path. This

threshold highly depends on the RF signal attenuation

with the distance traveled. It is also important to in-

vestigate better noise ﬁltering techniques to ﬁlter the

severe noise experienced at the testing scene. The lab

at which the experiments were conducted contain an

abundance of metal cabinets and obstacles of various

path

S1S2

D1D210L 10R

Left side

path

Right side

path

Y(m)

0.3 0.6 0.9 1.2 1.5 1.8 2.1 2.4 2.7 3.00

6.0

5.7

5.4

5.1

4.8

4.5

4.2

3.9

3.3

3.0

2.7

2.4

2.1

1.8

1.5

1.2

0.9

0.6

0.3

3.6

X (m)

Desired

Figure 4: Experimental setup of the proposed navigation

system.

materials. Such a choice was made on purpose to test

worst condition scenarios.

6 CONCLUSIONS

The rising prominence of location estimation in many

real-world applications necessitates the development

of an appropriate positioning system in an indoor en-

vironment. Due to the ubiquity of such localization

systems, the proposed RFID-based localization sys-

tem provides a suitable and a cost-effective solution

for devising such systems. In this paper, we have ex-

amined the problem of relative positioning system us-

ing RSS measurements of a modiﬁed RFID tag archi-

tecture and have proposed a novel guidance principle

for a mobile robot to navigate in an indoor environ-

ment based on the strength of the RF signal transmit-

ted by the RFID reader. As the ﬁrst contribution of the

TOWARDS A COMPUTATIONALLY EFFICIENT RELATIVE POSITIONING SYSTEM FOR INDOOR

ENVIRONMENTS - An RFID Approach

335

Table 1: Performance of the relative positioning system.

Position S1 S2 D1 D2 ∆RSS

1L 41 41 58 53 5

2L 44 44 58 54 4

3L 47 46 59 54 6

4L 50 45 55 54 6

5L 50 48 50 50 2

6L 51 52 49 46 2

7L 53 57 53 40 9

8L 53 58 50 41 4

9L 54 55 48 42 5

10L 63 60 43 38 8

Average 5.1

Std. Dev. 2.3

1 28 36 53 56 -11

2 42 38 54 57 1

3 44 45 50 50 -1

4 47 46 54 56 -1

5 50 46 52 54 2

6 51 52 49 49 -1

7 52 54 49 42 5

8 55 53 41 49 -6

9 57 56 38 44 -5

10 57 61 39 36 -1

Average -1.8

Std. Dev. 4.5

1R 37 42 62 57 0

2R 45 43 58 54 6

3R 43 47 56 51 1

4R 55 47 58 53 13

5R 54 51 49 49 3

6R 54 54 52 50 2

7R 56 57 49 48 0

8R 55 54 44 47 -2

9R 53 53 39 45 -6

10R 57 59 37 42 -7

Average 1.0

Std. Dev. 5.8

current work, spatial relative positioning is proposed

to address the variability of tags’ RSS patterns over

the workspace. The proposed method was evaluated

using real data from a typical ofﬁce environment. Al-

though the preliminary results reported in the present

manuscript reveal what might be a promising indoor

RPS method, more effort needs to be done to bring

the proposed technique to a more mature stage.

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