Towards Environmental Monitoring
with Mobile Robot
M. Trincavelli, M. Reggente, S. Coradeschi, A. Loutfi, A. Lilienthal,
AASS, Dept. of Technology, Örebro University, Sweden
Hiroshi Ishida
University of Agriculture & Technology, Tokyo Japan
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Gas Distribution Mapping Contents
Emerging need for environmental awareness in particular for air quality monitoring.
Investigate the ability to use mobile robots to address this need: Design of a pollution monitoring robot
How performance varies under different environmental conditions
Challenges for existing gas distribution mapping algorithms to cope with “real” and outdoor environments.
M. Trincavelli
Gas Distribution Mapping Contents
Emerging need for environmental awareness in particular for air quality monitoring.
Investigate the ability to use mobile robots to address this need: Design of a pollution monitoring robot
M. Trincavelli
Gas Distribution Mapping Contents
Emerging need for environmental awareness in particular for air quality monitoring.
Investigate the ability to use mobile robots to address this need: Design of a pollution monitoring robot
How performance varies under different environmental conditions.
M. Trincavelli
Gas Distribution Mapping Contents
Emerging need for environmental awareness in particular for air quality monitoring.
Investigate the ability to use mobile robots to address this need: Design of a pollution monitoring robot
How performance varies under different environmental conditions <add pictures of different environments>
M. Trincavelli
Gas Distribution Mapping Contents
Emerging need for environmental awareness in particular for air quality monitoring.
Investigate the ability to use mobile robots to address this need by: Design of a pollution monitoring robot
Investigating how performance varies under different environmental conditions
Investigate whether gas distribution mapping algorithms cope with “real” and outdoor environments.
M. Trincavelli
Gas Distribution Mapping Contents
Emerging need for environmental awareness in particular for air quality monitoring.
Investigate the ability to use mobile robots to address this need: Design of a pollution monitoring robot
How performance varies under different environmental conditions
Challenges for existing gas distribution mapping algorithms to cope with “real” and outdoor environments.
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Gas Distribution Modelling
Motivations – why mobile robots for pollution monitoring?
Oil Refinery Surveillance
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Gas Distribution Modelling
Motivations – why mobile robots for pollution monitoring? Oil Refinery Surveillance
Garbage Dump Site Surveillance
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Gas Distribution Modelling
Applications Oil Refinery Surveillance
Garbage Dump Site Surveillance
Urban Pollution Monitoring & Tracking air quality monitoring and surveillance of pedestrian areas
communicating pollution levels to technical staff / pedestrians
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Gas Distribution Modelling Enhance sensor networks by using robots to
provide higher resolution in measurement.
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Pollution Monitoring Robot
Kernel Based Gas Distribution Mapping
Experimental Setup
Experimental Results
Conclusion and Future Work
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Pollution Monitoring Robot
“Rasmus”
Contents
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Measure gases with SnO2 gas sensors
Actively ventilated sensor array
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Measure wind with a 3D ultrasonic anemometer
2cm/s – 40 m/s range, 1cm/s resolution
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Software is Player Based:
Monte Carlo Localization (amcl)
Obstacle Avoidance (vhf+)
Wavefront path planner
Consistent coordinate systems used to ensure
trajectory.
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Gas Distribution Mapping
in Natural Environments –
The Challenges
Contents
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Gas Distribution Mapping – Challenges
Chaotic Gas Distribution diffusion
advective transport
turbulence
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video by Hiroshi Ishida
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Gas Distribution Mapping – Challenges
Chaotic Gas Distribution
Point Measurement sensitive sensor surface is typically small
(often 1cm2)
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Gas Distribution Mapping – Challenges
Chaotic Gas Distribution
Point Measurement
Sensor Dynamics
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Gas Distribution Mapping – Challenges Chaotic Gas Distribution
Point Measurement
Sensor Dynamics
Calibration complicated "sensor response concentration"
relation
dependent on other variables (temperature, humidity, ...)
has to consider sensor dynamics
variation between individual sensors
long-term drift
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Gas Distribution Mapping – Challenges
Chaotic Gas Distribution
Point Measurement
Sensor Dynamics
Calibration
Real-Time Gas Distribution Mapping changes at different time-scales
rapid fluctuations
slow changes of the overall structure of the average distribution
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Kernel Based
Gas Distribution Mapping
Contents
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Kernel Based Gas Distribution Mapping
General Gas Distribution Mapping Problem given the robot trajectory
Differences to Range Sensing calibration: readings do not correspond directly
to concentration levels
3
),|( :: t1gas
t1gas zxmp
t1x :
M. Trincavelli
Kernel Based Gas Distribution Mapping
General Gas Distribution Mapping Problem given the robot trajectory
Differences to Range Sensing readings don't correspond directly to concentration
levels
chaotic gas distribution: an instantaneous snapshot of the gas distribution contains little information about the distribution at other times
3
t1x :
),|( :: t1gas
t1gas zxmp
M. Trincavelli
Kernel Based Gas Distribution Mapping
General Gas Distribution Mapping Problem given the robot trajectory
Differences to Range Sensing readings don't correspond directly to concentration
levels
instantaneous gas distribution snapshots contain little information about the distribution at other times
point measurement: a single gas sensor measurement provides information about a very small area ( 1cm2)
3
t1x :
),|( :: t1gas
t1gas zxmp
M. Trincavelli
Kernel Based Gas Distribution Mapping
Time-Averaged Gas Distribution Mapping Problem given the robot trajectory
Kernel Based Gas Distribution Mapping interpret gas sensor measurements zt as
random samples from a time-constant distribution assumes time-constant structure of the observed gas distribution
randomness due to concentration fluctuations (measurement noise negligible)
kernel to model information content of single readings
3
),|( :: t1gas
t1avgas zxmp
t1x :
Achim Lilienthal and Tom Duckett. "Building Gas Concentration Gridmaps with a Mobile Robot".
Robotics and Autonomous Systems, Vol. 48, No. 1, pp. 3-16, August 2004.
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Experimental Setup
Contents
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Experiments
For each environment:
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Experiments
For each environment:
Introduce an odour source.
Small cup filled with ethanol.
Placed on the ground in the middle of inspected area.
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Experiments
For each environment:
Introduce an ethanol outdour source
Follow a pre-defined sweep at 5cm/s measuring at stop points every:
10 sec (outdoor)
30 sec (indoor)
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ExperimentsFor each
environment:
Introduce an ethanol outdour source
Follow a pre-defined sweep at 5cm/s measuring at stop points.
Vary sweeping trajectory from different directions
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ExperimentsFor each environment:
Create a Gas Distribution Map.
Lighter shaded areas represent higher “concentration”.
Red regions represent relative concentrations levels above 80%.
Blue dots marks the location of measured highest concentration.
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Experiments
For each environment:
Overlay Wind Measurements.
Arrows coloured according to relative strength from blue to red.
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For each environment:
Overlay Wind Measurements.
Arrows coloured according to relative strength from blue to red.
Overlay spatial information.
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Experimental Results
Contents
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First Half
Second Half
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Initial experiments illustrate:
Difficulties of GDM mapping for real world applications without a ground truth.
The spatial distribution of a gas is unknown
Temporal distribution of the gas
Wind information can provide further clues about the results.
Gas distribution in real environments is a complex problem and this impacts many mobile olfaction applications.
Future work will need to examine the correlation between the instantaneous gas concentration and wind velocity vector in the GDM.