environmental odor perception- an evaluation of a platform based on labview and the lego nxt

Environmental odor perception: an evaluation of a

platform based on LabVIEW and the LEGO NXT

Alejandro R. García Ramírez #1

, Andy Blanco Rodríguez *2

, Armando O. López López *3

, Douglas W. Bertol#4

Alejandro Durán Carrillo de Albornoz*5

# Departamento de Engenharia de Computação, Universidade do Vale de Itajaí

Rodovia SC 407, Km 4, CEP 88120-000, São José, SC, Brazil 1 [email protected] 4 [email protected]

* Laboratorio de Investigaciones en Electrónica del Estado Sólido, Instituto de Ciencia y Tecnología de Materiales,

Universidad de La Habana

Zapata y G S/N, 10400, Vedado, La Habana, Cuba 2 [email protected]

3 [email protected]

5 [email protected]

Abstract— Environmental pollution, due to the man activity,

has generated an increasing interest in the development of

automated and intelligent systems for monitoring and analyzing

environmental variables. In this field, recent applications of

mobile robotics systems as well as electronic tongues and

electronic noses, based on an array of non-selective sensors and

artificial intelligence techniques (i.e. pattern recognition tools),

should be mentioned. There are known several practical

applications in demining tasks, exploring caves or tunnels, rescue

missions, drugs detection, toxic gas, explosive and human

detection and monitoring waters and air; as well as in

biotechnology, dairy, and food industries, among others. Also, it

is reported a great variety of mobile robotic hardware/software

platforms that has been studied by researchers nowadays. In this

work it is proposed an studied a mobile robotic architecture, for

odor detection, based on LabVIEW and the commercial LEGO

platform. So, it describes different concepts, the principal

methods and tools, as well as hardware features and software

algorithms commonly employed for the detection and

localization of odors which supports the proposed architecture.

I. INTRODUCTION

It is well known that the man, by means of his senses, and

usually helped by animals like dogs, performs the detection of

several chemical substances i.e. explosives, butane gas leaks,

fire smokes, etc. This is a complex task which, in some

applications, could involve certain risks also when the time of

detection constitutes a variable of interest i.e. localization of

people in catastrophic situations; when there is necessary long

periods of exposure (drug detection in airports) or when it is

impossible the man exposure as in source fire detection or

toxic gas leaks in tunnels. In this point, mobile robotics is

being considered an interesting tool for helping man, saving

time, improving human security and a better performance [1].

The development of robots is faster comparing to animals,

robots cost lower, they can work for a long period of time,

don´t suffer fatigue and they can move in risky or toxic areas.

The development of a mobile robotic system, which

performs the localization or detection of toxic substances in

the environment, is a complex problem because it includes the

navigation, localization and the robot hardware/software

control [2], but the design of a custom made electronic nose

(e-nose) [3], [4] as well as a data acquisition system [5], and

the processing [6], localization [7] and odor recognition

algorithms [8]. Research is very promissory in this field, being

the biology a new discipline to consider [9], [10] and it is

focused on improve the sensors response, to obtain a better

performance extracting the main features from the signals [11]

as well as developing new bio-inspired algorithms which also

improve the odor localization task [12]. This comprises the

use of computational fluid dynamics (CFD) tools [13], the

development of a robot architecture with its control algorithms

[14], [15] and also the simulation techniques [16]. Kowadlo

and Russell [17] review the state of the art in this topic.

There are reported different hardware/software platforms

for implementing mobile robots at universities and research

centers. These platforms can be divided into commercial or

custom made robotics systems. Custom made robots are

frequently microcontroller-based and the mechanical elements

are designed and assembled by the user [18]. Commercial

robots can be also divided in domestic i.e. AIBO and LEGO

RCX [19] or research robots like the Pioneer [20], Khepera

[21] or Koala [22] (www.k-team.com/). In general,

commercial robots are used to be closed platforms with

proprietary hardware/software resources. Recently there is a

trend to use the so called “development robotic platforms”

(hardware independent) and to migrate towards non-

propietary software like the Linux-based Player/Stage/Gazebo.

In this paper was proposed and validated some functional

features of a mobile robot architecture which is designed to

performs an odor localization task. The robot is based on the

LEGO NXT platform [23] and it also comprises two ethanol

sensors. The LEGO NXT offers some flexibility, but limited,

over its antecessor RCX and other closed platforms

concerning the mechanical project. It also allows the direct

connection of sensors from other manufacturers and it is also

ISSNIP Biosignals and Biorobotics Conference 2010 Theme: Biosignals and Robotics for Better and Safer Living Vitoria, Brazil - 4-6 January 2010

209

versatile during programming, which is very useful during the

experimental research. Different operating systems like Linux

or Windows; programming languages i.e. Java, C, G,

assembler and application programs (LabVIEW, Robolab,

Matlab) can be used during programming and simulation

stages. In 2006 the LEGO group released as open source the

firmware of the LEGO® MINDSTORMS® microprocessor as

well as the Software, Hardware and Bluetooth Developer Kits.

This proposal makes use of the proper NXT

hardware/software (ARM7/LabVIEW) resources during the

sensor conditioning, signal acquisition and processing stages

without the need of external circuits or processing boards.

This paper comprises six different sections. Section 1

addressed the odor source localization problem. Section 2

includes a description of the hardware and software resources

which supports the proposed architecture for odor detection.

The section 3 presents the robot design and the simple Virtual

Instrument developed using LabVIEW. In section 4 are

discussed the experimental results obtained through two

different experiments performed and in Section 5 are

presented the conclusions of this work and the future research.

At the last section, the bibliographic references are listed.

II. HARDWARE

This work proposes the design of a LEGO NXT and

LabVIEW-based mobile e-nose for odor detection (ethanol) in

a laboratory environment.

A. LEGO NXT

In 2006 the LEGO group put on the market the LEGO

Mindstorms NXT Robotic Kit. This set improves its RCX

previous version, still in use, allowing more complex

structures, designs and behaviors for robots and also useful in

sciences, technology, computing and engineering learning

[24]. The new NXT is based on the ARM-32-bit processor

AT91SAM7S256 and the ATMEGA48 co-processor. It also

comprises four inputs and three outputs ports as well as USB

and Bluetooth communication with the PC, as it is depicted in

Figure 1 (The LEGO group) [25].

Fig. 1. NXT hardware block diagram

B. Software

Different development environments were previously

mentioned though LabVIEW Toolkit for LEGO

MINDSTORMS NXT (National Instruments) was used for

programming and validating the application results, i.e.

ethanol sensors reading and robot movement toward the odor

source.

1) LabVIEW interface: The LabVIEW software

development environment allows implementing the so called

VI (virtual instrument): a kind of software-based instrument

supported by the PC hardware or by different devices or even

instruments connected via serial, USB, Bluetooth, PCI or

other hardware PC link interfaces. So the whole PC resources

could be used i.e. keyboard, mouse, communication ports,

display, network and Internet resources, processing

capabilities, etc. At the same time a custom made instrument

with flexible architecture (software-based), reusability,

modularity, and lower maintenance and development costs

comparing to traditional hardware-based instruments is

achieved.

2) NXT Toolkit: The Toolkit for LEGO MINDSTORMS

NXT is compatible with LabVIEW version 7.1 or higher. It

allows programming and controlling the NXT from the

LabVIEW environment, acquiring the signals on real time

from sensors and motors via USB/Bluetooth connection and

displaying they on the screen, to reutilize and design new

blocks (VIs) with new sensors or actuators, expanding the

system capability. This Toolkit presents two different toolset

or operation modes: NXT Toolkit and Direct Commands. The

first one includes controls, indicators and functions which

allow programming the NXT from LabVIEW. In this mode

the program is compiled and downloaded to the NXT for its

execution. It is also possible to send or receive data from the

PC (VI in execution) which is useful during debugging or

testing the application software running on the NXT. The

second operation mode allows programming and also running

the software in the PC under the LabVIEW environment, so

there are available the whole LabVIEW functions and its

programming resources (for developing more complex

projects), including the USB/Bluetooth connection too.

III. EXPERIMENTAL SET-UP

The robot architecture is based in a simple vehicle; it is

built using plastic LEGO blocks. Just two commercial ethanol

sensors were added (bilateral) to a known mobile robot design,

called TriBot, to carry out the application. It is also possible

to connect the optical and ultrasonic sensors from LEGO

manufacturer for obstacle avoidance, for example.

A. Sensors and actuators

The NXT input ports (1-4) allow connecting several digital

and analogue sensors. In this proposal two resistive ethanol

sensors, TGS2620, SnO2 based, from

(http://www.figaro.co.jp/) were employed. Figure 2 depicts

the gas sensor conditioning circuit. Sensor pins, 1 and 4,

supply a D.C. voltage level (VH=+5V / 40mA) to the filament


210

heater, necessary to carry out the physical transduction. The

sensor acts like a variable resistor, Rs, depending on the

ethanol concentration. This resistance variation is converted

into a voltage signal by a simple voltage divider attached to

the NXT analogue inputs 1-2, through the NXT internal

resistors R=10KΩ (connected to +5V), as it is depicted in

Figure 3. The NXT inputs 3 and 4 were reserved for

connecting the ultrasonic and optical sensors.

The output signal voltage at resistor RS corresponds to an

ethanol vapor concentration level.

Fig. 2. Gas sensor, TGS2620, conditioning circuit

The NXT output interface includes PWM as well as built-in

rotation sensors for localization tasks by using odometry. Two

NXT D.C. motors were connected to the output ports 1 and 3,

via output interface, so control commands and encoders

information can be sent and acquire, respectively, without

using any additional hardware.

Fig. 3. Hardware schematics

The motor power consumption, according to the LEGO

manufacturer, could reach 340 mA without load and 3A with

the motor loaded. A test were performed using six Lithium

batteries for energizing two motors to the maximal speed and

then changing the rotation sense after five seconds. In that test

the battery charge remains about one hour. Considering the

sensors power consumption (40 mA each) is it possible to

carry out the experimental works within one hour. Longer

working time could be achieved using the rechargeable LEGO

NXT battery.

B. The Robot

The robot architecture (TriBot) is based on a two motor

differential drive and a resting passive wheel, Figure 4. In this

paper the control algorithm for the odor localization task is

based on the LEGO [26].

Fig. 4. Robot picture and ethanol sensors

C. Programming

The control software was implemented by using the

LabVIEW 7.1 platform via NXT Toolkit functions, which

support sensor data acquisition and motors control. Figure 5

depicts a Braitenberg vehicle program for acquiring signals

from the gas sensors. The code was downloaded and executed

on the Lego NXT.

Fig. 5. A Braitenberg vehicle program using LabVIEW

This code reads, converts and displays (via debugging

mode), the gas sensory raw data to power, previously scaled.

Then, the motors run for an specified amount of time.

It means that the concentration value moves the robot,

which turns in the direction of the higher concentration level

of ethanol. Also if both sensors readings are equal (into

certain limits) the robot moves forward.

IV. RESULTS

Preliminary test were carried out to validate the acquisition

system. Figure 6 depicts the temporal profile corresponding to

a sequence presenting ethanol vapor at a distance of 20 cm to

the sensors. Following the graphs on the VI front panel it can

be noted that the sample is first presented to the left sensor


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(SENSOR 2), then to the right sensor (SENSOR 1) and at last

it is presented in front of the robot, almost at the same

distance from both sensors. It can be observed that the voltage

signal decrease when the ethanol vapor is detected.

Fig. 6. Temporal profile corresponding to a sequence presenting ethanol

vapor to the sensors

Two different experiments were carried out, as discussed in

the next subsections.

A. Reactive behavior test

When the program started, the vehicle moved slowly on

the floor, because there was a power value even in absence of

the analite (i.e.: air atmosphere.). When a plastic pot

containing ethanol was presented to each sensor in an

alternated sequence, 2 cm away, the vehicle turned in

response. Also, a turning delay was observed due to the gas

sensor slow response, which is related to the recovering time

of the sensors. The delay will be increased if the sample is

presented when the previous turning is not finished. Once the

recovering time had elapsed, when the sensors were receiving

the same stimuli or in absence of it, the vehicle moved straight

forward. The experiment showed a slight difference in the

signal amplitudes and recovering times (order of seconds)

between both sensors, which also disturb the vehicle

performance. The effectiveness percent for 50 tests was 98 %,

so, it can be concluded that our proposed vehicle reacts and

turns in response to the gas stimulus according to the

Braitenberg principle.

B. Odor tracking test

As it is depicted on Figure 7, the Braitenberg vehicle was

placed on the shorter side of a table (117 x 73 cm) and a

plastic pot containing ethanol was place at 88 cm away of it.

A straight line (goal line) was drawn perpendicularly to the

shorter side of the table, passing through the plastic pot and

two fans were located 5 cm behind the pot. Once the fans

were powered, the pot was shaken and 10 seconds later the

program started. The time the vehicle spends to reach the goal

line and the sensors reading was registered. The system was

refreshed between the experiments by removing the pot for 1

minute.

Fig. 7. E Set-up for the odor tracking test

In this case the vehicle exhibited a bad performance, as can

be concluded from the results showed in Table 1.

TABLE I

ODOR TRACKING TEST RESULTS

Number of

Test

performed

Reaching

the goal

(+/-4 cm)

Goal best

time (s)

Goal worst

time (s)

15 6 (40%) 17 28

The vehicle turns slightly to right even when it is not

exposed to the ethanol stimuli due to the difference in the

amplitude responses between both sensors. In order to

improve this performance it would be possible to compensate

the sensor responses applying some reported methods (i.e.

normalization, baseline correction, etc.). Also the adequate

processing of the signals could be accomplished by using i.e.

transient responses analysis, Wavelet or Fourier transforms

and would be also recommendable to apply more complex

robot control algorithms.

LabVIEW environment offers several advantages

(processing tools, displaying data, control functions, etc.)

when Direct Command functions are employed, but this set

offers a limited number functions for sensing and controlling

the NXT motors. On the other hand, the LEGO Toolkit

comprises several functions in order to control the motors and

sensing but the processing tools are only a limited subset of

LabVIEW resources.

V. CONCLUSIONS

Odor detection represents a serious challenge in mobile

robotics and its research and development have important

human and social applications. This research, even when it

was used a non research platform like LEGO NXT, confirms

different problems related to the task of odor localization, also

common to research robotic platforms: gas sensor and

conditioning circuits selection, navigation algorithms as well

as the robotic architecture employed in this application.

In this work, on the first stage, it was presented and

validated a LEGO NXT and LabVIEW-based robotic


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architecture, also comprising two ethanol commercial MOS

gas sensors for an odor detection task. The robot was

constructed using LEGO pieces, implementing a simple

Braitenberg vehicle, and the software control is a VI

programmed under LabVIEW platform, which supports

LEGO NXT commands.

The presented results confirm the LEGO NXT as a viable

platform to research in mobile robotic for odor detection in a

laboratory environment, but some limitations should be keep

in mind: limited resolution of the internal ADC (10 bits) for

low concentration vapor levels, nonlinearity introduced by the

conditioning voltage divider circuit, because the gas sensor is

connected directly to NXT input pins, the cost (about 300

Euros) and also the LEGO fragility which limits its use in the

field under real conditions (ground, moisture, temperature,

etc). However, LEGO NXT offers several advantages due to

the power of its computing ARM7 microprocessor: open

source hardware and software firmware, the possibility to

expand the system connecting new sensors and actuators and

also their integration with LabVIEW software which allows

(by default) multitask programming for robotics, visualization,

processing and offers powerful debugging tools. So, the latter

make LEGO NXT and LabVIEW scalable to professional

robotic studies.

Future works will consider improving and linearizing the

sensors responses, to add more complex control techniques

exploiting the ARM7 resources, implement complex

navigation tasks as well as obstacle avoidance algorithms.

MatLab/ Simulink and the Player/Stage open source platform

will be also other choices to explore for simulation and

implementation using the LEGO NXT.

ACKNOWLEDGMENT

This work was supported in part by the “Red Piloto de

Cooperación Universitaria mediante Red multidisciplinaria

con ámbito regional” (08CAP2.0655) and the “Universidad de

Alcalá de Henares” (Spain) for the support information, stay

and training resources put at the disposal of the authors. We

also thank the “Proyecto CAPES-MES 069/09 (Brazil):

Desarrollo de plataforma de robótica móvil y narices

electrónicas para la detección de sustancias en el medio

ambiente”, Mr. Raúl Coyula and Mss. Liliam Becherán. Our

acknowledgments to Mr. Edson Roberto De Pieri and to the

Departamento de Automação e Sistemas, at Universidade

Federal de Santa Catarina, by the support.

REFERENCES

[1] H. Ishida, T. Nakamoto, T. Moriizumi, T. Kinas and J. Janata, “Plume-

Tracking Robots: A New Application of Chemical Sensors,”

Biological Bulletin, vol. 200, pp. 222-226, April 2001.

[2] J. Borenstein, H. R. Everett and L. Feng, Where am I? Sensors and

Methods for Mobile Robot Positioning, J. Borenstein, Ed. The

University of Michigan, 1996.

[3] K. Arshak, E. Moore, G.M. Lyons, J. Harris and S. Clifford, “A review

of gas sensors employed in electronic nose applications,” Sensor

Review, vol. 24(2), pp. 181-198, 2004.

[4] L. Marques, A. Almeida, “Mobile robots olfaction,” Autonomous

Robot, vol. 20, pp. 183-184, April 2006.

[5] H. Ishida, K. Suetsugu, T. Nakamoto and T. Moriizumi, “Study of

autonomous mobile sensing system for localization of odor source

using gas sensors and anemometric sensors,” Sensors and Actuators A,

vol. 45(2), pp. 153–157, 1994.

[6] D. Compton, “Application of an Olfactory Data-Preprocessing

Algorithm to Chemotactic Robotic Navigation,” Journal of Young

Investigators, [Online]. Available:

http://www.jyi.org/research/re.php?id=1564, vol. 19(2), July 2008.

[7] A. T. Hayes, A. Martinoli and R. M. Goodman, “Distribution Odor

Source Localization,” IEEE Sensors Journal, vol. 2(3), pp. 260—271,

June 2002.

[8] R. L. Stewart, R. A. Russell and L. Kleeman, “Recognition and

Discrimination of Ethanol and Methanol Odour with Applications for

Robotic Swarm Control,” Monash University, Victoria, Australia,

Academic Research Forum of the Department of Electrical And

Computer Systems Engineering, Feb. 2003.

[9] W. J. Bell and T. R. Tobin, “Chemo-orientation,” Biological Reviews

of the Cambridge Philosophical Society, vol. 57, pp. 219–260, 1982

[10] N. Vickers, “Mechanisms of animal navigation in odor plumes,”

Biological Bulletin, vol. 198, pp. 203–212, 2000.

[11] R. Gutierrez, “Pattern Analysis for Machine Olfaction: A Review,”

IEEE Sensor Journal, vol. 2(3), pp. 189-202, 2002.

[12] T. Lochmatter and A. Martinoli, “A. Theoretical Analysis of Three

Bio-Inspired Plume Tracking Algorithms,” in Proc. IEEE International

Conference on Robotics and Automation, Kobe, Japan, May 12-17,

2009.

[13] J.A. Farrell, J. J. Murlis, X. Long, W. Li, and R. T. Cardé, “Filament-

Based Atmospheric Dispersion Model to Achieve Short Time-Scale

Structure of Odor Plumes,” Environmental Fluid Mechanics, vol. 2, pp.

143–169, 2002.

[14] V. M. Oliveira, E. R. Pieri and W. F. Lages, “Wheeled Mobile Robot

Control Using Sliding Modes and Neural Networks,” Learning and

Nonlinear Models, vol. 2, pp. 1-18, 2003.

[15] N. A. Martins, D. W. Bertol, W. C. Lombardi, E. R. Pieri and E. B.

Castelan, “Trajectory Tracking of a Nonholonomic Mobile Robot with

Parametric and Nonparametric Uncertainties: A Proposed Neural

Control,” International Journal of Factory Automation. Robotics and

Soft Computing, vol. 2, pp. 103-110, 2008.

[16] Z. Liu and T-F. Lu, “A Simulation Framework for Plume-Tracing

Research,” in Proc. Australasian Conference on Robotics and

Automation, Canberra, Australia, Dec. 3 - 5, 2008.

[17] G. Kowadlo, and R. A. Russell, “Robot Odor Localization: A

Taxonomy and Survey,” The International Journal of Robotics

Research, vol. 27, pp. 869-894, 2008.

[18] Meng, Q., Li, F., Sun, J., Bai, S. e Zeng, M. (2009). Multi-Robot

Based Odor Source Localization. RAS Newsletter – University of

Waterloo, Issue 7, January.

[19] B. Webb, “Robots, crickets and ants: models of neural control of

chemotaxis and phonotaxis,” Neural Networks 11(7-8), pp. 1479-1496,

1998.

[20] (2009) The mobile robots website. [Online]. Available:

http://www.activrobots.com/robots.html.

[21] T. Lochmatter, N. Heiniger, N. and A. Martinoli, “Localizing an odor

source and avoiding obstacles: Experiments in a wind tunnel using real

robots,” Proc. of the 13th International Symposium on Olfaction and

Electronic Nose, Brescia, Italy, April 15-17, 2009.

[22] D. Martinez, O. Rochel, and E. Hugues, “A biomimetic robot for

tracking specific odors in turbulent plumes,” Autonomous Robot.

Special Issue on Mobile Robot Olfaction, vol. 20(3), pp. 185-195, 2006.

[23] (2009) LEGO Mindstorm website. [Online]. Available:

http://mindstorms.lego.com/

[24] B. Bagnall, Maximum LEGO NXT: Building Robots with Java Brains.

Ed. Variant Press, 2007.

[25] The LEGO Group. (2006) “LEGO Mindstorm NXT Hardware

Developer Kit”, [Online]. Available:

http://mindstorms.lego.com/Overview/nxtreme.aspx pp. 1-25, 2006.

[26] V. Braitenberg, (1984). Vehicles: Experiments in Synthetic Psychology.

Boston, MA, Ed. The MIT Press, 1984.


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environmental odor perception- an evaluation of a platform based on labview and the lego nxt

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