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

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.

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

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.

M. Trincavelli Gas Distribution Modelling  Motivations – why mobile robots for pollution monitoring?  Oil Refinery Surveillance 1

M. Trincavelli Gas Distribution Modelling  Motivations – why mobile robots for pollution monitoring?  Oil Refinery Surveillance  Garbage Dump Site Surveillance 1

M. Trincavelli 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 1

M. Trincavelli Gas Distribution Modelling  Pollution Monitoring – DustBot Scenario 1

M. Trincavelli Gas Distribution Modelling  Enhance sensor networks by using robots to provide higher resolution in measurement. 1

M. Trincavelli  Pollution Monitoring Robot  Kernel Based Gas Distribution Mapping  Experimental Setup  Experimental Results  Conclusion and Future Work

M. Trincavelli Pollution Monitoring Robot “Rasmus”  Contents

M. Trincavelli  Measure gases with SnO2 gas sensors  Actively ventilated sensor array

M. Trincavelli  Measure wind with a 3D ultrasonic anemometer  2cm/s – 40 m/s range, 1cm/s resolution

M. Trincavelli  Software is Player Based:  Monte Carlo Localization (amcl)‏  Obstacle Avoidance (vhf+)‏  Wavefront path planner  Consistent coordinate systems used to ensure trajectory.

M. Trincavelli Gas Distribution Mapping in Natural Environments – The Challenges  Contents

M. Trincavelli Gas Distribution Mapping – Challenges  Chaotic Gas Distribution  diffusion  advective transport  turbulence 2 video by Hiroshi Ishida

M. Trincavelli Gas Distribution Mapping – Challenges  Chaotic Gas Distribution  Point Measurement  sensitive sensor surface is typically small (often  1cm 2 )‏ 2

M. Trincavelli Gas Distribution Mapping – Challenges  Chaotic Gas Distribution  Point Measurement  Sensor Dynamics 2

M. Trincavelli 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 2

M. Trincavelli 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 2

M. Trincavelli Kernel Based Gas Distribution Mapping  Contents

M. Trincavelli 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

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

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 (  1cm 2 )‏ 3

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 z t 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  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.

M. Trincavelli Experimental Setup  Contents

M. Trincavelli Experiments  For each environment: 5

M. Trincavelli Experiments  For each environment:  Introduce an odour source.  Small cup filled with ethanol.  Placed on the ground in the middle of inspected area. 5

M. Trincavelli 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)‏ 5

M. Trincavelli Experiments  For 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 5

M. Trincavelli Experiments  For 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. 5

M. Trincavelli Experiments  For each environment:  Overlay Wind Measurements.  Arrows coloured according to relative strength from blue to red. 5

M. Trincavelli  For each environment:  Overlay Wind Measurements.  Arrows coloured according to relative strength from blue to red.  Overlay spatial information.

M. Trincavelli Experimental Results  Contents

M. Trincavelli  INSERT MOVIE CLIP HERE

M. Trincavelli

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M. Trincavelli  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.