Achim Lilienthal
Gas Distribution Mapping and Gas Source Localisation with a Mobile Robot
Ph.D. Thesis, University of Tübingen, 2004
Abstract
This thesis addresses
two fundamental problems
concerning the application of electrochemical gas sensors on a mobile robot
in a real world environment:
gas distribution mapping
and localisation of a static gas source.
Apart from the limitations of current sensor technology,
the main difficulty for both of these tasks
results from the spreading of gases under natural conditions.
Diffusion plays only a minor role
in the distribution of odourant molecules
at room temperature.
The dominant transport mechanisms
are convection flow and
turbulence.
As a consequence,
the resulting concentration field is patchy and chaotic,
and the actual gas source is usually not located at the point of highest concentration.
In order to create a map of a gas distribution
it is thus required
to extract time-constant properties of the concentration field
from a series of sensor readings collected by a mobile robot.
In contrast to conventional mapping techniques,
where a considerable overlap between single measurements can be assumed,
gas sensor measurements
provide information about a very small area.
The problem of gas distribution mapping is therefore
to create a representation of the average concentration field
from sparse point samples of the instantaneous distribution
with little or no overlap between single measurements.
A new algorithm is introduced in this work
to create a representation
that stores belief about the average relative concentration of a detected gas
in a grid structure.
The problem of gas source localisation can be broken down into three subtasks:
gas finding, source tracing and source declaration.
Gas finding
-- the detection of an increased concentration --
amounts to a basic search task and the selection of a suitable threshold value,
a problem that is not addressed in this work.
The main part of this thesis
is rather concerned
with the latter two aspects:
following the cues
determined from the sensed gas distribution
towards its origin
(source tracing)
and establishing that the source has been found
(source declaration).
In order to avoid limitation
to an environment with a strong airflow,
the investigated strategies
do not rely on information about wind direction and speed.
Accordingly,
the experiments were carried out
in an unventilated indoor environment
where previously suggested methods for gas source localisation,
which include periods of upwind movement,
are not applicable
due to the limitations
of current anemometers.
A straightforward solution for gas source tracing
is to follow an instantaneously measured spatial concentration gradient.
Such a strategy was realised on a mobile robot
by implementing a direct sensor motor coupling
in the manner of a Braitenberg vehicle.
The particular contribution of this work is
a detailed statistical evaluation of the tracing performance
based on a large number of experiments.
While gradient following is often mislead by transient concentration maxima,
it could be shown with high statistical significance
that the path length required to reach the source
can be reduced on average
compared to random search.
As a further gas source tracing method,
a biomimetic strategy was investigated
that is based on the key elements of the behaviour
of male silkworm moths to find a mate guided by sexual pheromones.
A modification for use on mobile robots is proposed
that does not depend on information about the local wind speed.
The performance of this tracing strategy
was tested in a largely uncontrolled indoor environment
and evaluated by statistical means.
Despite the considerably larger size of the robot compared to the moth,
the results suggest
that the modified strategy
decreases the average robot-to-source distance
compared to random exploration,
because it can keep the robot in the vicinity of a gas source
after single gas patches have been discovered by initial exploration.
In order to address the full gas source localisation problem,
a tracing strategy has to be extended
by an additional declaration mechanism
to determine that the source has been found with high certainty.
It is generally not sufficient
to search for maxima of the instantaneous concentration distribution
in order to solve the declaration task.
However,
it was demonstrated
that peaks in sensor response
can provide a rough estimate of the gas source location
if the search space is restricted.
In experiments in a one dimensional scenario,
the sensing strategy was found to have a dominant influence.
A strong correlation between sensor response and proximity to a source
could be obtained only
if the robot was driven with a constant, sufficiently high speed,
while such a correlation could not be observed
if a stop-sense-go strategy was applied.
A possible explanation for this effect
suggests an alternative feature
that might be used to recognise a gas source.
Rather than looking for a global maximum of concentration,
a gas source can be distinguished by an increased frequency of local maxima.
This was also found
in experiments in a two dimensional scenario
where the sensor-motor connections of the implemented gas-sensitive Braitenberg vehicle were crossed.
In this way,
the robot performs exploration and concentration peak avoidance,
resulting in a path
that reflects the frequency of local maxima in the inspected area.
A visualisation of this path
offers therefore a possible method for gas source declaration
without using additional sensors.
Finally,
the thesis investigates the possibility
to classify a suspected object
as being a gas source or not
from a pattern in a series of spatially and temporally sampled concentration data.
Such a pattern was determined by applying machine learning techniques.
The results of this ongoing work demonstrate
the feasibility of the approach
and show that high classification rates can be achieved
using support vector machines.
An analysis of
the most important features for classification,
the dependency of the classification rate on the desired declaration accuracy,
and a comparison with the classification rate that can be achieved by selecting an optimal threshold value regarding the mean sensor signal
is also presented.
Bibtex
@PHDTHESIS{Lilienthal:2004,
AUTHOR = "Achim Lilienthal",
TITLE = {{Gas Distribution Mapping and Gas Source Localisation with a Mobile Robot}},
SCHOOL = {Wilhelm-Schickard Institute, University of T\"ubingen},
YEAR = "2004"
}