DESIGN OF A PROTOTYPE ROBOT VACUUM CLEANER

From Virtual Prototyping to Real Development

Leire Maruri, Ana Martinez-Esnaola, Joseba Landaluze

IKERLAN Research Centre, Arizmendiarrieta 2, E-20500 Arrasate (The Basque Country), Spain

Sergio Casas, Marcos Fernandez

ARTEC Group, Valencia University, E-46980 Paterna, Spain

Keywords: Robot vacuum cleaner, Virtual prototype, Fuzzy logic, SIL (Software-In-the-Loop).

Abstract: This paper presents the prototype of a robot vacuum cleaner designed and constructed by IKERLAN. It

details, above all, the hardware and software components used, as well as the navigation algorithm,

designed using fuzzy logic. In conjunction to this an existing virtual prototype of the robot and the domestic

environment was updated with a view to fine-tuning and testing the real controller of the autonomous robot

by means of SIL (Software-in-the-Loop) simulations. Finally, some of the position estimation problems that

arose in the experimental tests are described.

1 INTRODUCTION

Modern living brings with it the need to use time as

efficiently as possible, one of the key objectives

being to create as much free-time for ourselves as

we possibly can. This explains why people

increasingly surround themselves with appliances

capable of carrying out essential, yet distinctly

unappealing, household chores. It is here that

domestic appliances have a role to play.

Until not so very long ago, a washing machine

and fridge were considered sufficient. However,

when these household appliances, along with the

tumble dryer, hair dryer, microwave, vacuum

cleaner, etc. became commonplace in the home, they

were required not only to perform the task for which

they were designed, but to do so in the simplest and

most efficient way possible. What is more, demand

is increasing with every passing day for new

household appliances capable of making even the

most tedious jobs bearable, such as ironing or

cleaning in general. In an ideal world, the domestic

appliances of today would even be replaced by new

gadgets with ability to perform tasks in a completely

independent way, which is where concepts such as

domotics and domestic robots come in.

One such device is the robot vacuum cleaner, a

very active area of research for several years now,

with many prototypes having been developed, some

of which are now appearing on the market (Irobot,

2006; Electrolux, 2006; Karcher, 2006).

The robot vacuum cleaner is a special case

among mobile robot systems. Taking as a starting

point the principle of a robot capable of navigating

reactively in theoretically unknown environments

and designed to avoid variable obstacles, a number

of different versions of domestic cleaners can be

obtained (ones that vacuum the floor, wash dishes,

polish furniture, etc.) by fitting the requisite

accessories. In order for a robot vacuum cleaner to

take its place among the numerous domestic

appliances on the market today, however, it must

meet some very specific requirements with regard to

price, simplicity, ease of use, independence from the

surrounding environment and cleaning efficiency

among others.

Over the last few years IKERLAN and the

ARTEC Group at the University of Valencia have

been working on the design of a domestic robot,

particularly as an example of the application of HIL

(Hardware-in-the-Loop) technology in the design of

new industrial products. Research was conducted on

the navigation system (sensors, actuators, and

navigation and sweeping algorithms) and tested

using the virtual prototype of a generic robot

vacuum and a domestic environment in HIL

simulation (Fernandez, 2003; Martinez, 2004).

461

Maruri L., Martinez-Esnaola A., Landaluze J., Casas S. and Fernandez M. (2006).

DESIGN OF A PROTOTYPE ROBOT VACUUM CLEANER - From Virtual Prototyping to Real Development.

In Proceedings of the Third International Conference on Informatics in Control, Automation and Robotics, pages 461-468

DOI: 10.5220/0001201004610468

 SciTePress

More recently, a prototype robot vacuum was

designed and constructed. The virtual prototype of

the mobile robot and the domestic environment was

also improved. Among other things, an accurate

model of the vacuum cleaner was added, other

dynamically changing elements were included (such

a ball that appears in the environment) and a version

for Windows/PC was created. As a result, the

control programme designed was fine-tuned and

tested using the virtual prototype, following a SIL

(Software-in-the-Loop) procedure.

This paper presents the most significant aspects

of the robot vacuum cleaner prototype that has been

designed and constructed; its navigation system,

based on fuzzy logic; and the improved version of

the virtual prototype. First of all, the paper briefly

reviews the current situation with regard to research

on robot vacuum cleaners, placing special emphasis

on the designs that are currently available on the

market. It then goes on to describe the hardware and

software contained in the robot vacuum cleaner

designed, as well as the navigation strategy. After

detailing the virtual prototype, the paper concludes

by presenting some of the position estimation

problems that arose in the experimental tests.

2 BRIEF REVIEW OF ROBOT

VACUUM CLEANERS

After years of fits and starts, the market for robot

housemaids finally seems to be taking off. New

models of robot vacuum cleaners are the first signs

that a nascent commercial robot industry is finally

taking hold. However, the robot vacuums now being

sold are not designed to replace the vacuum cleaner

altogether. The manufacturers describe their

products as "maintenance" or "continuous" cleaners

(Kahney, 2003). There are a lot of models of robotic

vacuum cleaners available today, and they range in

price from $50 all the way up to $1,800. These

robots are typically low-slung and compact, meaning

they can get under furniture normally inaccessible to

regular upright vacuum cleaners.

There are three leading robot vacuum companies:

Europe’s Electrolux and Karcher, and the United

States’ iRobot (Figure 1). Other cleaning

manufacturers like Japan’s Hitachi and the UK’s

Dyson are working on the development of this kind

of robot, as are Friendly Robotics, FloorBotics,

Hanool Robotics, Samsung and Lentek.

The most popular robotic vacuum in the United

States is iRobot's Roomba, which comes in various

models ranging from the base-model Roomba Red

($150) to the super high-tech Roomba Scheduler

($350). It is a round-shaped vacuum-device that

works like a pool cleaner, bouncing around a room

until it covers all - or most of - the floor (Kahney,

2003). The cleaning system includes a spinning side

brush that cleans along walls, two counter-rotating

brushes that capture large debris and a vacuum that

picks up dust. At the end of a cleaning cycle or when

the battery is running low, Roomba returns to the

Home Base to recharge. When dirt hits one of the

two sensors located immediately above the brushes,

it turns towards the dirty area, vigorously cleaning

the area most in need of attention. It also includes

infrared sensors that prevent Roomba from falling

off ledges and stairs. The system includes virtual

walls that create an infrared signal that Roomba will

not cross, keeping Roomba where you want it to

clean (Irobot, 2006).

Electrolux's Trilobite is more complex, and more

expensive. The $1800 Trilobite creates an internal

map of the room as it cleans, and recharges

automatically when its power reserves are low. The

type of cleaning programme you want can be

selected (normal, quick or spot) and it has a start

button for immediate cleaning, or alternatively

cleaning times may be set in advance. The Trilobite

sends out harmless ultrasonic signals to spot objects

and avoid them. It begins by edging along the walls

to map the size of the room, whilst simultaneously

cleaning the edges. It then proceeds to clean the

Figure 1: Commercial robot vacuum cleaners: Trilobite, RoboCleaner and Roomba.

ICINCO 2006 - ROBOTICS AND AUTOMATION

462

whole room. A suspension device prevents the

machine from getting stuck, and a special sensor

stops it from falling down stairs (Electrolux, 2006).

Kärcher's RoboCleaner (Karcher, 2006) is

available for about 1100€. It takes a "random-walk"

around the house, sensing walls and obstacles with

its touch-sensitive bumpers. It avoids stairs, and is

low enough to fit under most furniture. A pair of

"rubber ears" on top prevents it from getting stuck.

The RoboCleaner monitors the stream of incoming

dirt and concentrates on especially dirty spots. The

owner need only empty the recharging station's dust

bag when it gets full.

The British company Dyson is trying to develop

a robot cleaner (DC06) and has gone back to the

R&D phase until they can make an affordable,

autonomous vacuum cleaner.

Japanese electronics giant Hitachi is working on

a similar product that also acts as a home security

guard. The robot cleaner can work independently or

manually, controlled by computer or cell phone. An

in-built camera allows the owner to monitor his

house over the Internet while away. It comes with a

charging station, to which it returns to recharge its

batteries and dump its dust load. The robot creates a

map of the house's layout as it moves around. It

remembers the layout of furniture and which areas

of the house have been cleaned and which have not.

It has a retractable 2-inch hose for cleaning in

corners. The machine bristles with sensors (light,

heat and all-around bumpers) for detecting hazards

and preventing it from getting stuck in corners.

3 ROBOT VACUUM CLEANER

DEVELOPED

3.1 Hardware Structure

Design and development of the robot vacuum

cleaner prototype was based on off-the-shelf, low-

cost components. The Rex-12 Round Robot Base

with Encoders from Zagros Robotics (2006) was

chosen for the mobile structure and support of all the

components. It consists of a desk base 30 cm in

diameter, which has two drive wheels and is

supported on two casters. The drive wheels are 15

cm in diameter and the two caster wheels 7.5 cm in

diameter. The base includes two 12 V drive motors

for the drive wheels, and each motor has a 500

pulses per revolution HEDS encoder.

The platform can easily carry over 15 kg of

payload at a maximum speed of 24 m per minute.

The sensor system uses 16 units of the SRF08

Ultrasonic Rangefinder Sensor from Robot

Electronics (2006). Communication with the

ultrasonic sensors is via an I2C bus. A BrainStem

GP1.0 Module from Acroname (2006) is used to

read the ultrasonic sensors and measure distances to

obstacles. The distances measured are sent to the

robot controller by means of a RS-232 serial port. In

the prototype a car vacuum cleaner has been used to

suck up dust.

The control unit consists of a sandwich of

PC/104 modules. The modules included are as

follows:

• A CPU board, MOPSIcd7-700 MHz from

Jumptec (2006), with 512 MB of SDRAM and

an IDE compatible Flash-Disk of 96 MB.

• ESC629ER Dual DC Servo Motor Control

Board from RTD (2006) to control the drive

motors of the desk base. It has also 24 TTL

level I/O lines which are used to control the car

vacuum cleaner. A customised circuit was

developed for that purpose.

• PCM-3110 1-Slot PCMCIA Module from

Advantech (2006).

• Instant Wireless Network PC Card from

Linksys (2006) for wireless communications.

• HESC104 Module from Tri-M Engineering

(2006), a DC-to-DC 60 watt converter for

embedded applications that supplies ±5 V and

±12 V.

• BAT104-SLA45 Battery Pack from Tri-M

Engineering (2006). This is sealed, lead acid

battery backup unit for HESC104 power supply.

It consists of 5DD x 4.5A Hr batteries with

digital temperature sensors.

The battery pack and the HESC104 unit supply

the power needed by the drive motors, the car

vacuum cleaner and the control unit itself.

By means of the wireless communication link,

the robot vacuum cleaner programme is loaded and

monitoring data acquired.

Figure 2 shows a photograph of the robot

vacuum cleaner prototype developed, as well as its

main components.

Figure 2: Robot prototype and its components.

DESIGN OF A PROTOTYPE ROBOT VACUUM CLEANER - From Virtual Prototyping to Real Development

463

3.2 Software Structure

The Operating System used in the CPU board of the

mobile robot is eLinOS v2.1, an embedded version

of Linux, with RTAI extension for hard real-time.

The control program is created in a host computer

and then downloaded through the wireless link.

The robot controller has been developed in

Simulink, where it is tested using a simple virtual

environment consisting of a circular or rectangular

room. After ensuring it is operating correctly, the

code of the navigation algorithm only is created

using the RTW (Real Time Workshop) utility. This

code is then integrated into the application along

with the rest of the functionality (reading sensors

and the sending of commands to the motors) and

compiled for eLinOS and Windows. This controller

is embedded in the mobile robot or can be integrated

into the virtual prototype in order to test it.

The controller carries out three basic functions: it

receives information from the sensors on the

distances measured; it periodically calls the

navigation and control algorithm (created with RTW)

to obtain the wheel velocity commands; and it sends

these commands to the motors.

Figure 3 shows the development diagram of the

embedded application, representing the application

as the “Control” component built into the CPU

PC/104, and showing the interaction with the

peripherals fitted on the robot vacuum cleaner.

The application functions as per the sequence

diagram in Figure 4. As soon as it starts up, the

application configures an interruption timer before

starting the input and output (sensors and motors)

and configuring the operating system signals. At the

end of the initialisation process, it enters a loop that

performs two of the basic functions: it reads

information from the sensors and sends the velocity

commands to the motors. In conjunction, the

operating system periodically sends (every 10 ms)

signals via the rigalrm (alarm signal) function.

These signals are captured by the programme and

trigger the call to the “navigate” function, which

implements the navigation control.

4 NAVIGATION SYSTEM

The navigation system, implemented in

Matlab/Simulink, provides a navigation algorithm

capable of locating a wall in an unknown

environment and following it, as well as determining

when the boundaries of the enclosed area it is

moving in have been defined. It then begins a

sweeping phase in which it covers most of the

enclosed area in an efficient manner. The navigation

system consists, therefore, of three basic stages:

identifying the enclosed area to be swept; the

sweeping of internal areas; and the bordering of

obstacles. Figure 5 shows the statechart diagram of

the navigation application.

In the initial stage the robot performs a square

spiral during which it moves by alternating between

straight lines and right-angle turns until it finds a

wall. At this point it moves to the next stage known

as “wall following”, which, as its name suggests,

involves the robot skirting along walls. Throughout

this phase the robot is controlled by the fuzzy

control system, which attempts to guide it at a fixed

distance from the wall, delimiting the enclosed area

in question (Urzelai, 1997).

Once it has been established that the “wall

following” phase has marked off an enclosed area

(as a result of readings received from the motor

Figure 3: Development diagram of the application.

sd Requirements

Main Con trol TimerMotors

Con trol :

Indefinite Loop

Sensors

Navi gate :

Navi gati on

Algorithm Created

with RTW from

Simulink.

Initialize

Create T imer

Con figure OS

Initialize

Control

Rea d

SetReference

Timeout

Navigate

Figure 4: Sequence diagram of the application embedded.

ICINCO 2006 - ROBOTICS AND AUTOMATION

464

encoders), the third and final phase in the

application, known as “sweeping”, is performed.

Here, the algorithm encloses the trajectory taken by

the robot in a cell matrix that enables it to identify

the swept area and, on the basis of this, a decision

strategy informs it of which cells need to be swept at

each point. If at any time an obstacle is detected, the

robot switches back to wall-following status and

adopts a fuzzy control strategy (the same as the one

used in that particular stage).

4.1 Fuzzy Wall-following Control

The “wall following” function features three distinct

modules or parts: pre-processing, fuzzy control and

post-processing.

Pre-processing introduces the concept of the

perception vector (Figure 6). This vector indicates

the proximity of an obstacle and its direction. The

angle of the perception vector is that formed by the

direction the robot advances in. The module of the

vector expresses the distance the robot is from the

wall in a standardised way using an ideal distance

value: 1 if the robot is at the wall, and 0 if it is

double the ideal distance or further. As a result, the

fuzzy control system attempts to keep the module of

the perception vector at 0.5, equivalent to the ideal

distance. The output in this pre-processing block

consists of the module of the perception, the angle of

the perception, the derivative of the perception and

the loss of perception. This last output indicates

whether the robot has switched from measuring

perception to a value of 0 (i.e. when the robot moves

further than it should from the wall).

The navigation module calculates the velocity

commands for the motors. To be able to do this it

features two strategies represented by the first two

stages in the statechart diagram (Figure 5): the first

being a wall-search strategy, and the other a wall-

following strategy designed to identify the sweeping

area. These two control modes are selected

according to the value of the perception vector

module. If the module has a value equal to one, the

control system will search for a wall and will

attempt to follow a square spiral pattern until one is

found, at which point the loss-of-perception signal

resets to zero and the robot attempts to follow the

wall thanks to the wall-following control function

performed by the fuzzy block.

In the search for walls, the square spiral is

generated as a result of two types of commands sent

to the wheels: one in which the robot advances in a

straight line, with the same command being sent to

the two wheels; and the other where the robot rotates

90°, with one wheel receiving a certain velocity

command and the other the same command but with

a different sign, thereby ensuring that the robot

rotates on its axis without lateral displacement.

When the perception value is anything other than

zero (meaning that it has found a wall), the fuzzy

block performs the control. This block has the

“module of the perception” and the absolute value of

the “angle of the perception“ as inputs, thereby

simplifying the fuzzy model, as it is the symmetry of

the problem that is used. The system uses three

fuzzy variables: modP (module of the perception);

angP (angle of the perception); and Giro (the output

variable for the robot’s angle of rotation).

The membership functions of the modP (module

of the perception) input variable appear in Figure 7a.

As stated above, this variable provides an estimate

of the distance to the wall. The membership

functions of the angP (angle of the perception)

variable appear in Figure 7b. As has already been

mentioned, this input is always positive, as it takes

the absolute value, making use of the system’s

symmetry. In the event of this angle being negative,

the sign is changed.

The only output in the block is Giro: the rotation

that the robot must perform to prevent it from

running into, or approaching too close to, the wall,

depending on the value of the module of the

sm Requirements

Initial

Spiral

Wall Follow ing

Sw ee ping

Final

Obstacle

Conto ur Closed

!Wall

Wal l

Figure 5: Statechart diagram of the “navegate” function.

Figure 6: Perception vector and perception angle.

DESIGN OF A PROTOTYPE ROBOT VACUUM CLEANER - From Virtual Prototyping to Real Development

465

perception. By incorporating the following rules into

the fuzzy controller (Figure 7c), the robot vacuum

cleaner tries to maintain an angle of 90º between the

direction of advance and the wall, being the distance

to the wall the ideal one.

modP/angP

Front Medium Back

Low L1 R2 R4

Medium L3 Z R3

High L4 L2 R1

Having established the rotation the robot must

perform, the post-processing module calculates the

velocity commands to be sent to each of the wheels.

4.2 Sweeping Strategy

Existing robot vacuum cleaners on the market that

have to move around unknown environments mainly

use two navigation strategies. The first involves

random navigation, i.e. the robot moves in a straight

line until it encounters an obstacle and then changes

direction randomly, continuing in a straight line

again until coming across the next obstacle. This

approach, although not particularly efficient, is very

easy to be implemented. The second approach

involves mapping the environment as the robot

moves so that a reference to the robot position can

be obtained and the optimal sweeping strategy

defined. The latter approach was that chosen for the

design of the sweeping algorithm.

Once the contour has been determined, it is

demarcated by a rectangle with as small a surface

area as possible. This area is divided into cells with a

predetermined size. Each cell has a status: the cell

can belong to the contour; the outside of the contour;

or the inside of the contour (Figure 8). As a result,

the input is restricted to the inside of the contour.

From this point on, the behaviour of the

prototype is no longer controlled by fuzzy rules. The

robot proceeds to use the matrix obtained in the first

stage to calculate the points of its next path.

Therefore, some of the cells will change their status

from “inside” to “path”. The cells with the “path”

status are converted into “swept” cells. If the room

were completely empty, the robot would sweep the

room in a spiral.

The process of sweeping inner areas is repeated

continuously until 3% of the area initially selected

for the task is left. At this point, the prototype

detects the zones that are still unswept, known as

"islands". The robot chooses only those “islands”

that are larger than the size of the robot vacuum

cleaner. Then the robot selects the “island” nearest

to its position and approaches it by the shortest

route. Each island then becomes a “small room” to

be cleaned, and sweeping is controlled by the same

rules of behaviour. Once all the islands have been

swept, the robot considers the task completed.

5 VIRTUAL PROTOTYPE OF

ROBOT VACUUM CLEANER

As stated above, one of the main objectives of the

research carried out by IKERLAN and ARTEC was

to validate the use of HIL methodology in designing

and implementing the real prototype. Thus, in order

to provide a realistic environment in which to test

the robot vacuum cleaner, a real-time realistic 3D

simulation was developed alongside the real

prototype. This allowed us to test the HIL strategies

Figure 7: Membership functions of fuzzy variables: a) Perception distance; b) Perception angle; c) Turning angle.

Figure 8: a) Cells outside of the contour; b) Cells on the

spiral and the contour; c) Cells to be swept.

ICINCO 2006 - ROBOTICS AND AUTOMATION

466

by having a better feedback and a visual first

impression of how the control system behaved.

5.1 Description

This virtual prototype is a standalone, real-time,

graphic/dynamic simulation written in C++ that runs

on both Windows and Linux operating systems.

The virtual prototype simulation (hereinafter

referred to as “simulation”) is a multi-threaded real-

time oriented application composed of three main

subsystems (Fernandez, 2003):

• 3D real-time graphic subsystem: an OpenGL

Performer-based module responsible for

providing realism and visual quality to the

application. It takes advantage of the scene-

graph representation of the visual information

while using advanced graphic techniques to

enhance the user’s sense of “immersion”.

• Dynamic simulation subsystem: given the task

of providing the application with realistic

Newtonian physics; based on the Open

Dynamics Engine (ODE).

• Communication subsystem: the module where

the simulation communicates with the control

system in order to get actuator feedback and

provide sensor information.

5.2 Improvements in Relation to the

Existing Prototype

Several changes have been made to the architecture

of the existing prototype (Fernandez, 2003;

Martinez, 2004); the most apparent of which is the

migration to a Windows-based application.

A subtle, yet important, difference with respect

to the existing prototype is that this revised version

of the simulation is able to run independently from

(provided it is supplied with values for the motor

velocities), or alongside, the control system

developed by IKERLAN. This link can be made

with either the hardware prototype or the software

controller that substitutes it, and, as such, HIL

(Hardware-in-the-Loop) becomes SIL (Software-in-

the-Loop).

Little has changed with respect to the existing

version of the graphic subsystem, although new,

more complex 3D models of both the robot vacuum

cleaner and the environment (with a new set of

furniture and home-related items to test the robot

over more challenging conditions than it was done

using the previous prototype) were built.

The dynamic subsystem, nonetheless, has

undergone some dramatic changes in order to adapt

it to the new robot configuration designed by

IKERLAN. This includes some new joints, the

redesign of the locomotion system and a new (and

very necessary) stabilization system. Without this,

the simulation would have not been able to meet the

new time requirements imposed by the increased

complexity of the new graphic models. Likewise,

random objects (such as a football, Figure 9a) have

also been introduced into the simulation, in order to

test the ability of the robot to react to unexpected

events and objects which suddenly appear in its path.

Finally, the communication subsystem was also

changed in order to provide a software interface with

the Simulink-based software version of the

controller. This allows us to test the navigation

software directly against the virtual prototype,

instead of having to load it into a hardware platform.

The solution could thus be termed a loophole within

the Software in the Loop.

6 EVALUATION OF RESULTS

The existing virtual prototype described in

(Fernandez, 2003) and (Martinez, 2004) is simpler

and was used to evaluate different sensing

Figure 9: Virtual Prototype: a) General view with a random object; b) Swept area.

DESIGN OF A PROTOTYPE ROBOT VACUUM CLEANER - From Virtual Prototyping to Real Development

467

alternatives and navigation strategies. The

conclusions reached (Martinez, 2004) provided the

starting point for the design and construction of the

real prototype described and presented in this paper.

Once the robot had been designed (Figure 2), the

virtual prototype of the robot and the domestic

environment was updated and enhanced (Figure 9),

as described in section 5. The new virtual prototype

was used to test, above all, the controller in the real

prototype and particularly the navigation and

sweeping strategies. The activation of a trace

mechanism showing the robot’s path, as shown in

Figure 9b, was extremely useful for checking the

effectiveness of the sweeping algorithm.

By the end of the process, the real prototype of

the robot vacuum cleaner had been tested

experimentally in simple wall-following and living-

space sweeping tasks. The results for wall following

were positive (Figure 10), although variations were

detected in the estimation of the distance to the wall

whenever the wall material changed (e.g. when the

robot passed in front of a wooden door). By contrast,

the results for the sweeping tasks were worse due to

errors in estimating position using measurements

from the wheel encoders. These errors led to the fact

that the algorithm used for the sweeping of enclosed

areas was inefficient. The main reason for this was

that due to the wheels slipping the measurements

provided by the wheel encoders were inaccurate.

This problem had already been detected in the SIL

simulation with the virtual prototype whenever there

was a change in the surface friction coefficient (e.g.

when the robot moved from a rug onto parquet

flooring, Figure 9b), but was much more serious in

the real prototype.

To draw conclusions from the experimental tests

conducted: the sensing system must be modified or

completed before moving on to a commercial

prototype so that an accurate estimate of the real

position of the robot (the basis of the designed

sweeping algorithm) can be obtained.

7 CONCLUSIONS

This paper presented the prototype of a robot

vacuum cleaner designed and constructed by

IKERLAN. It detailed, above all, the hardware and

software components used, in addition to the

navigation algorithm, the design of which was based

on fuzzy logic. Moreover, an existing virtual

prototype of the robot and its domestic environment

were updated, thereby enabling the fine-tuning and

testing of the real in-built control of the autonomous

robot using SIL (Software-in-the-Loop) simulations.

Finally, the problems arising from the experimental

tests conducted were described in detail, and the

conclusion reached that the sensing system must be

improved so that the real position of the robot,

which forms the basis of the sweeping algorithm

designed, can be estimated accurately.

ACKNOWLEDGEMENTS

The material used in this paper was partly funded by

the Spanish Ministry of Science and Technology and

FEDER (research project DPI2002-04438-C02-01).

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Zagros Robotics. 2006. www.zagrosrobotics.com

Figure 10: Real Prototype in the wall-following task.

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