aass learning systems lab, Örebro...

AASS Learning Systems Lab, Örebro University

Matteo Reggente and Achim J. Lilienthal

Contents

1. Background

2. Experimental Setup

3. Gas Distribution Model

4. Experiments and Results

5. Ongoing and Future Work

Contents

Background

1 Pollution Monitoring in Urban Enviroment

Stationary Pollution Monitoring

Courtesy: The London air Quality Network – King’s College London

1 Mobile Robot with electronic-nose (“m-nose”)

Advantages of Using Mobile Sensor Nodes"m-nose" can act as a wireless node in a sensor network Monitoring with higher resolutionPossibility of source trackingIntegration into existing applicationCompensation for inactive sensorsAdaption to dynamic change of the environmentCan be used at dangerous places

1 DustBot

Project Reference Number: FP6 -045299

Networked and Cooperating Robots for Urban Hygiene

1 DustBot

Project Reference Number: FP6 -045299

Networked and Cooperating Robots for Urban Hygiene

http://www.dustbot.org/

1 Gas Distribution Modelling

Gas Distribution ModellingTask of deriving a truthful representation of the observed gas distribution from:

a set gas sensor readings and position

Very Challenging!!!

1 CFD Models Approach

Computional Fluid Dynamics (CFD) modelsTry to solve the governing set of equations numerically.

They are computationally very expensive.

They become intractable for sufficiently high resolution in typical real world settings.

They depend sensitively on accurate knowledge of the state of the environment(Boundary Condition), which is not available in practical situations.

1 Statistical Approach

Parameter-Free ApproachMakes no assumptions about a particular functional form of the gas distribution.Statistical model of the observed gas distribution.Treats gas sensor measurements as random variables.Creates a statistical model discretized to a grid map.

1 Related Work

3D – Gas Tracking with Blimp

H. Ishida et al., “Three-Dimensional Gas/Odor Plume Tracking with Blimp,” in Proc. ICEE, 2004.

1 Related Work

Tracking Chemical Plumes in 3-Dimensions

R. Andrew Russell, “Tracking Chemical Plumes in 3-Dimensions” , in proc. 2006 IEEE International Conference on Robotics and Biomimetics

1 Related Work

Tracking Chemical Plumes in 3-Dimensions

H. Ishida.et al., “Three-Dimensional Gas-Plume Tracking Using Gas Sensors and Ultrasonic Anemometer,” in IEEE Sensors (2004), 2004, pp. 1175–1178.

Contents

Experimental Setup

2 Experimental Set-up: The Robot

Pollution Monitoring RobotiRobot ATRV – JR (all terrain robot)

PlayerStage to interface sensors and actuators

B. Gerkey, R. T. Vaughan, and A. Howard, The Player/Stage Project: Tools for Multi-Robot and Distributed Sensor Systems, Proc. ICAR, 2003, pp. 317–323.

2 Experimental Set-up

Pollution Monitoring Robot

Nose A (10cm)

Nose A at 10 cm

2 Experimental Set-up: The “e-noses”

Pollution Monitoring RobotNose A at 10 cm

Nose B (60cm)

Nose B at 60 cm

2 Experimental Set-up: The “e-noses”

Pollution Monitoring RobotNose A at 10 cmNose B at 60 cm

Nose C (110cm)

Nose C at 110 cm

Figaro 2620

Rasmus

Contents

Gas Distribution Model

3 Gas Distribution Model – Problem Statement

Learn a predective model ( )nzzp :1,n:1xx,

Kernel DM+V treats distribution modelling as a density estimation problem

no assumptions about the particular functional form of the modelled gas distribution

3 The Kernel DM+V Algorithm – step 1

ωi(k)(σ)

Weights represent the importance of

sensor measurement iat grid cell (k).

They are computed using

a multivariate Gaussian

kernel.

ωi(k)(σ) = N (|xi – x(k)|,σ)


ωi(k)(σ)

Ω(k)

R(k)

V(k)α(k)

Confidence map indicates high confidence for cells if the estimate can be based on a large number of readings.

α(k)= 1-exp(-(Ω(k))2/σΩ2)


ωi(k)(σ)

Ω(k)

R(k)

V(k)α(k) r(k)

v(k)

Distribution Mean Map

Distribution Variance Map

r(k)= α(k)(R(k)/Ω(k))+{1-α(k)}r0

v(k)= α(k)(V(k)/Ω(k))+{1-α(k)}v0

3 Extension to the 3D : 3D Gaussian Kernel

2D Model

2D Model


⎥⎥⎥

⎦

⎤

⎢⎢⎢

⎣

⎡

=Σ2

2

2

000000

z

y

x

σσ

σ




α(k) )(σx, σy, σz)

r(k)

v(k)

Confidence map

α(k)= 1-exp(-(Ω(k) )(σx, σy, σz))2/σΩ2)




α(k) )(σx, σy, σz)

r(k)

v(k)

Distribution Mean Map

Distribution Variance Map

Contents

Experiments and Results

4 Experiments: The Scenario

Experimental Scenario Gas Source:Small cup filled with Ethanol or AcetonOn the ground and at height of 160 cmPre-defined sweeping

trajectory and random walk (5cm/s)Measurements while

driving and stoppedExchange of air

with the "outer world"

4 Corridor_20081023a

Gas Source: Ethanol at 160 cm

Random Walk

4 Corridor_20081014a


Sweep

4 Corridor_20081014a – 3D Visualization

4 Classroom_20080911a


Sweep

4 Evaluation

Ground truth Validation?

3D Sensors Grid?

Gas Selective Infra Red Camera (Optical Gas Imaging)?

http://www.atmeco.com/OGI.html

4 Statistical Evaluation

2D Model


Kullback-Leibler (KL) divergence

( ) ∫−= dxxpxqxpqpKL)()(ln)(

ModelledDistribution

Unknown Distribution

4 KL vs Kernel Geometry

Contents

Ongoing and Future Work

5 Wind+3D DM

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