NAVIGATION SYSTEM FOR INDOOR MOBILE ROBOTS BASED

ON RFID TAGS

Toshifumi Tsukiyama and Atsushi Suzuki

School of Information Environment,Tokyo Denki University, Inzai, Chiba, Japan

Keywords:

Mobile Robot, Navigation, RFID Tag, IC Memory, Landmark, Topological Map.

Abstract:

A new navigation method is described for an indoor mobile robot. The robot system is composed of a Radio

Frequency Identiﬁcation (RFID) tag sensor and a commercial three-wheel mobile platform with ultrasonic

rangeﬁnders. The RFID tags are used as landmarks for navigation and the topological relation map which

shows the connection of scattered tags through the environment is used as course instructions to a goal. The

robot automatically follows paths using the ultrasonic rangeﬁnders until a tag is found and then refers the

next movement to the topological map for a decision. Our proposed technique would be useful for real-world

robotic applications such as intelligent navigation for motorized wheelchairs.

1 INTRODUCTION

This paper describes a navigation system for mobile

robots which are assumed to move autonomously to

a given goal in man-made environments such as hall-

ways in a building. A key function of the navigation

system is to choose a direction to a goal at a particular

place such the intersection of two hallways in a build-

ing. The navigation system requires a mechanism for

recognizing such particular places in the building and

locating them on a world map that gives course direc-

tions to a goal.

Two common approaches to robot navigation are

metric-based and landmark-based navigation (Mur-

phy, 2000). Metric-based navigation relies on met-

ric maps of the environment, resulting in naviga-

tion plans such as move forward ﬁve meters, turn

right ninety degrees and move forward another eight

meters. For position-sensing schemes, this ap-

proach relies on dead-reckoning based on informa-

tion about the motion of the robot derived from the

wheel encoders, or absolute position estimation us-

ing the global positioning system (GPS) (Hofmann-

Wellenof, 2003). These metric data are, however,

likely to be corrupted by sensor noise and this naviga-

tion method is vulnerable to inaccuracies in position

estimates.

To avoid reliance on error-prone metric data,

an alternative approach is landmark-based naviga-

tion. Landmark-based navigation relies on topolog-

ical maps whose nodes correspond to landmarks (lo-

cally distinctive places) such as corridor junctions or

doors. Map edges indicate how the landmarks con-

nect and how the robot should navigate between them.

A typical landmark-based navigation plan might be

to move forward to the junction, turn into the corri-

dor on the right, move to its end, and stop. This may

involve a complete absence of metric data and then

the method does not depend on geometric accuracy

(Kawamura, 2002). It has apparent analogies with hu-

man spatial perception, so it is easy to make a map of

the environment. In addition, topological representa-

tions avoid the potentially massive storage costs asso-

ciated with metric representations.

One problem to be solved in this method is to

decide what are suitable for landmarks in the envi-

ronment. Landmarks should be a distinctive one and

easy to recognize without special costs. In a building

intersections, corners and doors are very important

places for navigation and they could be a landmark.

However, they are often repetitively similar and suf-

fer from problem of occasionally sensors not being

able to distinguish between similar landmarks, such

as different doors of the same size. This can lead to

298

Tsukiyama T. and Suzuki A. (2007).

NAVIGATION SYSTEM FOR INDOOR MOBILE ROBOTS BASED ON RFID TAGS.

In Proceedings of the Fourth International Conference on Informatics in Control, Automation and Robotics, pages 298-304

DOI: 10.5220/0001644002980304

 SciTePress

both inefﬁciency and mistakes. Although such land-

marks with an artiﬁcial sign could be reliable and use-

ful, painted marks on walls would require special im-

age processing to extract them from the scene. This

would entail a complicated and costly process.

Therefore, we propose a method using Radio Fre-

quency Identiﬁcation (RFID) tags as a sign. The

RFID tags are a passive, non-contact, read-only mem-

ory system based on electromagnetic wave and can

store a unique number for identiﬁcation of the loca-

tion. The tags allow the acquisition of location infor-

mation at remarkable speeds without any distance in-

formation and the accurate control of robot positions

for sensing landmarks. Micro-processors with a wire-

less modem may be possible to give location infor-

mation as a landmark (Fujii, 1997). Although they

have the ability to handle data processing, they need

an on-board power supply like a battery. Landmarks

should be embedded in the environment and offer a

virtually unlimited operational lifetime. The passive

RFID tags operate without a separate external power

source and obtain operating power generated from the

reader. The tags are consequently much lighter, thin-

ner and less expensive than the active devices with a

microprocessor. The RFID tags can give enough lo-

cation information to achieve the robustness and efﬁ-

ciency of navigation.

2 RFID TAGS AS LANDMARKS

Figure 1 shows an RFID tags and the antenna box

of the RFID tag sensor system on a mobile robot.

The RFID tag (shielded in a 12 cm square plastic

plate) is an IC memory (115 Bytes) with a built-in an-

tenna, which is pasted on walls at particular places in

a building. Each IC memory has a unique ID number

which can provide information on its location within

the building. Figure 2 illustrates the way to get ID

number in a tag through the RFID tag sensor system

on the robot. The RFID tag sensor consists of an RF

transceiver and an antenna. The RF transceiver illu-

minates an RFID tag with a short pulse of electro-

magnetic waves (2.45 GHz, 0.3 w). The tag receives

the RF transmission, rectiﬁes the signal to obtain DC

power, reads its memory to get the ID number, and

modulates the antenna backscatter in response to the

interrogation. The RF transceiver obtains the ID num-

ber and reports it to the navigation system running

on a Linux computer through an RS-232c serial port.

Figure 3 illustrates the sensitivity map of the RFID

tag sensor system. Since the induction area of a tag

is 40 cm wide and 100 cm depth from the antenna the

robot does not need precise positioning mechanisms

to locate and access the tags. The robot just passes by

tags without the accurate control of position for sens-

ing their numbers.

Figure 1: RFID tag pasted on a wall (white panel on right)

and the antenna of a RFID tag sensor mounted on a mobile

robot.

Wireless

Comunications

Memory

(ID Number)

Antenna

Electromagnetic

Wave (2.4GHz)

Energy

RFID Tag

(Transponder with no Battery)

RF Transceiver

with Decoder

Data

DC Power Generator

RS232c

Figure 2: Architecture of the RFID tag sensor system.

3 MAP FOR NAVIGATION

Figure 4(a) shows an example of a ﬂoor plan in a

building. The letters a, b, c, d, e, f, g, and h in the

ﬂoor plan denote the intersections of two hallways,

the junctions or near the door, which can be a par-

ticular place for a mobile robot. At these places the

robot has to choose an action to reach a given goal;

left-turn, right-turn, straight ahead, U-turn or stop if

the place is the destination. The robot repeats one of

these actions at every particular place. The particular

places can be considered a sub-goal. Global tasks (i.e.

NAVIGATION SYSTEM FOR INDOOR MOBILE ROBOTS BASED ON RFID TAGS

299

40 cm

100

50 cm

40 cm

x, y

Antenna of RFID tag sensor

Figure 3: Sensitivity range of the RFID tag sensor system.

e f

(b)

(a)

door

passage

Figure 4: Example of a ﬂoor plan (a) and its topological

map (b).

going to a distant goal) require a world map for de-

ciding a sequence of sub-goals to a given goal. Figure

4(b) describes the topological relation of these partic-

ular places in the building. The nodes of the graph

with a letter correspond to the particular places such

as an intersection in the ﬂoor plan, respectively. An

edge between two nodes denotes that a hallway ex-

ists and a robot can pass along it to the next particular

place. Using graph search techniques you can gener-

ate a sequence of particular places to lead the robot to

a given goal, if a starting node is given on the graph.

The topological relation of particular places through

hallways can be used as a world map.

To distinguish particular places in the building we

(a)

2122

Node c

Direction Node Tag ID

N d 8

E h 9

S b 10

W a 7

Node d

Direction Node Tag ID

N ? 12

E e 13

S c 11

W -- --

(b)

Figure 5: Conﬁguration of RFID tags for the ﬂoor plan (a)

and examples of the data structure of a node (b).

use the RFID tags. The tags are pasted on the left side

walls near the intersections of two hallways, the junc-

tions or doors. Actually, it is very difﬁcult to paste

a tag precisely in a ﬁxed position in a hallway or to

measure its position. The role of tags is to give just

information that the robot is coming upon an intersec-

tion and what the name of the intersection is. Figure

5(a) illustrates the conﬁguration of the tags in the ﬂoor

plan. The dots near particular places show the posi-

tions of RFID tags and the numbers are an ID number

which the navigation system uses to identify upcom-

ing intersections in the robot path. The scattered tags

through hallways are used as a cue to decide the next

action. In our scenario the robot moves along the left

side of hallways ﬁnding tags and then the navigation

system recognizes the robot’s location on a world map

based on the tag’s ID number and decides the next

movement of the robot toward a given goal.

In path planning the navigation system must de-

cide a travel direction to the next sub-goal at an in-

tersection. To do this the order of the adjacent edges

which join at a node should be explicitly described

in the world map. In other words this means to de-

scribe on the map which hallway is on the left or right

side with respect to one hallway. One possible way

is to draw up a list of hallways in the data structure

of each node, maintaining the clockwise order of the

hallways. However, this is likely to confuse the order

when you trace the topological map from an entire

ﬂoor plan. In our scenario robots pass through hall-

ways in a building. They are usually intersected like

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a cross. Therefore, we use compass points such as

North (N), South (S), East(E), West (W) for a rule of

the notation of directions. When you make a world

map, ﬁrst, you should assign the North direction on

the ﬂoor plan for the reference bearing.

The data structure of a node include the list of ad-

jacent nodes with a compass point and tag ID numbers

which a robot will ﬁnd coming to a particular place.

Figure 5(b) illustrates examples of the data structure

of a node of the graph. The mark ”–” in the node d

denotes there is no hallway in this direction, because

the node d is a junction. The mark ”?” means that a

hallway exists but the name of an adjacent node is un-

known at the moment. If a robot ﬁnds a tag with the

ID number 9 in a hallway, for example, the navigation

system searches all the node data of the graph for the

tag number and then ﬁnds it is in the data structure

of the node c. Consequently, the navigation system

recognizes the robot is coming into the intersection

c from the East side. Suppose that the next sub-goal

is the junction d, the navigation system searches the

node c for the next node direction. It ﬁnds that the

node d is adjacent to the node c and the direction is

the North. The robot is coming from the East side,

turns to the right at the intersection and going out to

the North side. Finally it will ﬁnd another tag with

the ID number 11. This process is repeated until the

robot ﬁnds the goal.

We have developed an interactive system for mak-

ing the database of map information from a ﬂoor plan.

The system was built up on a Linux computer with the

graphical toll kit GTK+ and the graphical user inter-

face (GUI) is shown in Figure 6. The small window in

the upper right corner displays the menus for editing.

The main window is used to draw the topological con-

nection of intersections and junctions the robot can

pass through. First, the user assigns the North direc-

tion on the ﬂoor plan for the reference bearing. The

mouse button is clicked on the screen and the mouse

is dragged in one of the four directions (North, South,

East or West), then two nodes with an edge is dis-

played and the data structures for the nodes as shown

in Figure 5(b) are created in the system. After this the

mouse button is clicked on one of the nodes and the

mouse is dragged to extend the graph. Next, the user

changes the editing mode to compile the data struc-

ture of a node and clicks each node on the screen.

Then another small window (in the lower left corner)

appears to input the RFID tag numbers which are set

near the node. This process is repeated for every par-

ticular place on the ﬂoor plan. Finally, the user selects

the menu to save the database in a ﬁle, which the robot

can use for path planning.

Figure 6: Interface for tracing a map.

4 ROBOT SYSTEM

Figure 7 illustrates the architecture of the navigation

system. The system mainly consists of a navigation

planning module, a graphical user interface (GUI) for

tracing maps and a database of map information. The

GUI as shown in Figure 6 enables us to make the

database of map information from a ﬂoor plan. The

database is a set of the data structure of nodes shown

in Figure 5(b), which shows the relation of the par-

ticular places of the ﬂoor plan and assigned RFID

tag numbers to each particular place. The navigation

planning module decide a route to a given goal from

sensed RFID tag numbers and the map information.

The navigation system was implemented on a host

computer running on Linux and the mobile robot

shown in Figure 1. The host computer manages the

functions of the navigation planning module shown in

Figure 7. The computer has two wireless RS-232c se-

rial ports. It is responsible for handling the data from

the RFID tag sensor through a wireless serial port and

sending commands to the motor control module on

the micro-controller of the mobile robot through an-

other wireless serial port. The robot consists of a mo-

bile platform and an RFID tag sensor. The mobile

platform (approx. 35 cm square) is equipped with an

on-board micro-controller, a two-wheel drive system

with one rear free wheel, an odometer with optical en-

coders, seven ultrasonic rangeﬁnders and an RS-232c

serial port. The rangeﬁnder units are mounted at the

front of the robot as shown in Figure 8(a) for forward

and lateral sensing and the sensitivity range is up to

5 meters. The micro-controller manages the motion

control module for running the drive system and col-

NAVIGATION SYSTEM FOR INDOOR MOBILE ROBOTS BASED ON RFID TAGS

301

lecting position and speed information from the drive

encoders, including ﬁring the sonar sensors and re-

trieving echo signals.

In our method the precise positions of RFID tags

in a hallway is not known. Also, the robot is not

aware of the length of hallways. The robot just fol-

lows the left side walls of the hallway until it ﬁnds

a tag. It is essential that an mobile robot be able to

realign itself relative to a wall and then proceed if it

becomes disoriented. Man-made environments such

as hallways are usually constructed with a horizontal

plane (a ﬂoor area) and vertical planes (walls, pillars

and doors). The boundary line formed by the ﬂoor

and a wall becomes a long straight line which can be

seen at any point in the scene. Since the RFID tags

are pasted on vertical planes, the walls, the boundary

lines can be used as a guide for navigation. The geom-

etry of sensing the distance to a wall and measuring

the orientation of the robot with respect to the wall

is shown in Figure 8(b). The robot is equipped with

seven rangeﬁnders as shown in Figure 8(a). The lat-

eral rangeﬁnder views the left side wall at two points

and measure the distance (d1, d2) to the wall. The

orientation of the robot with respect to the wall is cal-

culated from the difference of the distances and the

moving distance L.

Figure 9 shows the ﬂow of motion control for the

robot. The rangeﬁnders are invoked every few meters

of movement and generate the angle formed by a wall

and the robot’s direction and the perpendicular dis-

tance to the wall from the robot. The navigation plan-

ning module uses these data to change the orientation

of the robot parallel to the wall and drives it along the

wall maintaining the distance between the robot and

the wall. The rangeﬁnders also check for sudden ob-

stacles for emergency stops. If a tag is detected while

the robot is moving the navigation planning module

recognizes the robot’s location on the topological map

from the ID number and then decides the next direc-

tion: the robot turns to the left, turns to the right, goes

ahead or turns back. When changing the direction at

an intersection the movement is controlled mainly by

the rangeﬁnders. The robot moves forward measuring

the distance to both side walls with the rangeﬁnders

and ﬁnd the center of the intersection from the mea-

sured distance. At the center the distance becomes

huge because of no walls. Then, the robot turn to the

indicated direction. After the robot passes an intersec-

tion the rangeﬁnders are invoked again and the robot

follows another hallway until it ﬁnds a tag.

RFID Tag

Sensor Module

Navigation Planning Module

Motor Control

Module

Graphical

User Interface

Sonar

Rangefinder

Odometer

Database of

Map Information

Figure 7: Architecture of the navigation system.

Front of robot

(a)

Direction

of ultrsound

Wall

(b)

Figure 8: Sonar positions (a) and measuring the orientation

of the mobile robot(b).

5 EXPERIMENTAL RESULTS

A typical test run of the mobile robot based on the

navigation method is shown in the series of pho-

tographs in Figure 10. The sequence shows the robot

starts near an RFID tag, orients itself in a junction and

then traveling until it ﬁnds another RFID tag. This

movement is similar to one along the route from the

tag number 23 to 19 in Figure 5(a). The width of the

passage was 1.6 m and the traveling distance along

the passage was about 8 m. As shown in Figure 10(a)

a tag was ﬁxed on the low pole near the exit to a pas-

sage. Figure 10 (b) shows that the robot passed by the

tag and the tag’s ID number was detected by the RFID

tag sensor. The navigation planning module decided

next movement based on the tag’s ID number. In this

test run the robot was scheduled to turn to the right.

The robot moved for a while under the control of

the odometer of the robot and then checked if the right

side of the robot is a free space (i.e. no walls) using

the ultrasonic rangeﬁnders. This process was repeated

until an enough space was found on the right side (see

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Start

Measure Distance to Walls

Check RFID Tag Number

Check Route Map

Tag No. > 0

Decide Direction and Move

End

Goal ?

Follow Passage

Yes

Figure 9: Flow of the navigation method.

Figure 10(c) and (d)). After that the robot turned to

the right as shown in Figure 10(e) and (f). The robot

moved along the passage (see Figure 10(g), (h) and

(i)) and after every 1 m of movement, the rangeﬁnder

module was invoked. From the geometrical informa-

tion the navigation planning module decided the ro-

tation angle of the robot to reorient the robot parallel

to the wall, and the distance needed to draw the robot

near to the wall. To sense the RFID tags reliably the

robot must move along the wall at a distance of less

than 1 m. In this experiment the distance was 0.8 m.

The sensor guidance with iterative sensing and mo-

tion continues until an RFID tag is found. In Figure

10(j) the robot suddenly faced the wall obliquely due

to slip or something. The navigation plan module at-

tempted to reorient the robot parallel to the wall (Fig-

ure 10(k)). After that, another RFID tag was detected

and the robot stopped.

6 CONCLUSIONS

Mobile robots are a very imprecise mechanism. Navi-

gation systems for mobile robots should have a mech-

anism to accomplish tasks with an adequate degree of

precision. We thus proposed a topological navigation

system using RFID tags and a sonar rangeﬁnder sys-

tem. The rangeﬁnder was used to reorient the robot

parallel to a wall keeping the distance to it. We also

introduced a topological connection map of the RFID

tags, which can be built without precise 3-D represen-

tation of the environment. Robots just follow the tags

under instructions of the topological map. The equip-

ment setup is very simple and the navigation system

is easily combined with the robot’s computer systems.

This is both practical and acceptable for the applica-

tion of mobile robots to the real world.

A graphical user interface for making the world

map conveniently is indispensable for the application

of the navigation method. We have built an interac-

tive system running on the Linux. This is an on-going

project.

REFERENCES

Murphy, R. R. (2000). Introduction to AI Robotics. MIT

Press.

Hofmann-Wellenhof, B and et al. (2003). Navigation - prin-

ciples of positioning and guidance. Springer.

Fujii, T. and et al.(1997). Intelligent Data Carrier System

for Cooperative Behaviors Emerge among Collective

Robots. Proc. of IEEE International Conference on

Systems, Man, and Cybernetics, Vol. 1, pp. 299-304.

Kawamura, k and et. al.(2002). Toward Perception-Based

Navigation Using EgoSphere. Proc. of SPIE, Vol.

4573, pp. 137-147.

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(a) (b) (c)

(d) (e) (f)

(g) (h) (i)

(j) (k)

Figure 10: Test run of the mobile robot.

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