modeling mos gas sensors for mobile robot...
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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
0 10 20 30 40 50 600
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time(s)
No
rmal
ized
val
ues
gas concentrationsensor response1st source sensor response
RELATED WORK: exponential model
Smoke source
0 50 100 150
0
25
50
75
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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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Time(s)
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sen
sor r
espo
nse
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Time(s)
Nor
mal
ized
gas
dis
tribu
tion 0
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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
10 15 20 25 30 35
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Time (s)
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ized
sen
sor r
espo
nse
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Nor
mal
ized
gas
dis
tribu
tion
Negative Concentrations ?
PROPOSED MODEL : Compensations
1. Two phase model (rise / decay) 2. Speed compensation (delay) 3. Dynamic time constants
50 60 70 80 90 100 110 120 130
Time(s)
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Nor
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sor r
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nse
Nor
mal
ized
gas
dis
tribu
tion
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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)
Nor
mal
ized
Val
ues
PROPOSED MODEL : Experiments
Speed = 10cm/s Gas = Acetona Go & return
Modeled Raw Readings 0 1 2 3 4 5 6 7 8 9 0
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Distance (m)
Nor
mal
ized
Val
ues
PROPOSED MODEL : Experiments
Speed = 40cm/s Gas = Acetona One way
Modeled Raw Readings
TGS2620 Reading Gas Source Position
0 2 4 6 8 10 12 0
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Distance (m)
Nor
mal
ized
Val
ues
PROPOSED MODEL : Experiments
Speed = 40cm/s Gas = Acetona Go & return
Modeled Raw Readings 0 2 4 6 8 10 12 0
0.2
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1
Distance (m)
Nor
mal
ized
Val
ues
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!