MODELING MOS GAS SENSORS FOR MOBILE ROBOT OLFACTION

Javier G. Monroy, Javier González and Jose Luis Blanco

Dep. System Engineering and Automation UNIVERSITY OF MÁLAGA - SPAIN

INTRODUCTION: e-nose

Cyranose 320

Looking back...First ENose Space Flight launched October 29, 1998!

MCE-nose

SensorFreshQ electronic nose

INTRODUCTION: general applications

INTRODUCTION: mobile robotic applications

Robot

Gas mapping

Track following

Leak Detection

• What we expect from gas sensors... – high sensitivity – large dynamic range – high selectivity / specificity to a target analyte – low cross-sensitivity to interferents – perfect reversibility of the physicochemical sensing

process • short sensor response and recovery time

– long-term stability – "a sensor exhibiting all these properties is a largely

unrealizable ideal" → Higher-Order Chemical Sensing, A. Hierlemann and R. Gutierrez-Osuna. Chem. Rev. 2008, 108, 563-613.

GAS SENSORS: What for?

GAS SENSORS: technologies

MOS

Infrared

SAW

Pellistor

Electromechanical

GAS SENSORS – MOS: How they work?

• Metal Oxide Gas Sensor (MOS) – heating element – coated with with semiconductor sensing material

• often tin dioxide

– sensing material doped with catalytic metal additives • e.g. palladium or platinum • doping changes operating conditions → sensor characteristics

Semiconductor Coating (typically SnO2)

Heating Element

Semiconductor Coating (typically SnO2)

Heating Element

GAS SENSORS – MOS: How they work?

GAS SENSORS – MOS: Pros & Cons

Figaro sensors

e2v sensors

CHEAP HIGH SENSITIVITY

x LACK OF SELECTIVITY x RESPONSE DRIFT(AGE FACTOR) x INFLUENCED BY TEMPERATURE AND HUMIDITY x LONG ACQUISITION CYCLES (SLOW RECOVERY)

GAS SENSORS – MOS: Selectivity improvement

GAS SENSORS - MOS: Other improvements

LONG ACQUISITION CYCLES (SLOW RECOVERY)

Normalization Signal conditioning

RESPONSE DRIFT(AGE FACTOR)

INFLUENCED BY TEMPERATURE AND HUMIDITY

New materials Humidity and temeprature sensors

Hardware MCE-nose Software Modeling

GAS SENSORS : MOS long recovery time

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gas concentrationsensor response1st source sensor response

RELATED WORK: exponential model

Smoke source

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Ideal Step Concentration

Time (s)

Con

cent

ratio

n (%

)

τr τd

Ideal MOS sensor response

Two phase model

Exponential model

PROPOSED MODEL: goal?

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PROPOSED MODEL

Gas (ppm) Resistance (Ohms) Readings (v)

PROPOSED MODEL

Gas (ppm) TRANSDUCER (ppm V) Readings (v) MOS behaviour (MODEL)

Gas distribution estimation (v)

PROPOSED MODEL : Experiments (validation)

For validation we need….

GROUNDTRUTH

We need the real gas distribution and concentration!

PROPOSED MODEL : Experiments (validation) • Chaotic Gas Dispersal

- Diffusion -Advective transport -Turbulence

[Smyth and Moum, 2001]

PROPOSED MODEL : Experiments (validation)

KEEP THE GAS LOCALIZED

PROPOSED MODEL : Experiments (validation)

Clean Air

Odor Airflow

PROPOSED MODEL : Experiments (validation)

PROPOSED MODEL : Compensations

1. Two phase model (rise / decay) 2. Speed compensation (delay) 3. Dynamic time constants

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Negative Concentrations ?

PROPOSED MODEL : Compensations

1. Two phase model (rise / decay) 2. Speed compensation (delay) 3. Dynamic time constants

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PROPOSED MODEL : Experiments

Robot motion

Robot motion

Gas sources

Raw readings

Gas Distribution estimation

PROPOSED MODEL : Experiments

PROPOSED MODEL : Experiments

WHAT HAS BEEN DONE TILL TODAY? • Slow down the robot (few cm/s). • Pass several times over the same locations but along different directions.

Robot motion

Gas source

PROPOSED MODEL : Experiments

Speed = 10cm/s Gas = Acetona One way

Modeled Raw Readings 0 1 2 3 4 5 6 7 8 9 0

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Distance (m)

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ues

PROPOSED MODEL : Experiments

Speed = 10cm/s Gas = Acetona Go & return

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PROPOSED MODEL : Experiments

Speed = 40cm/s Gas = Acetona One way

Modeled Raw Readings

TGS2620 Reading Gas Source Position

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PROPOSED MODEL : Experiments

Speed = 40cm/s Gas = Acetona Go & return

Modeled Raw Readings 0 2 4 6 8 10 12 0

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PROPOSED MODEL : Conclusions

Conclusions The proposed model overcomes the long-decay-time problem of MOS sensors. Improves sensing task wit mobile robots (accuracy, robot speed, task time reduction) Future Work Improve the calibration of the model parameters. Exploit the model in mobile robotics olfaction: multiple gas source finding, plume tracking, … Compare the results with the MCE-nose

THANKS & QUESTIONS

THANKS!