Thesis
Contributions
The
project focussed
on using naïve physics for odour localisation
in a cluttered indoor environment. The main contributions of the
research are
as follows:
-
A
new approach to Naïve Physics, encapsulating intuitive rules
into an algorithm
in order to model an aspect of the environment. This was the
first example of Naïve Physics for a practical robotics
application.
-
The
identification and analysis of a new problem domain for odour
localisation -
'enclosed spaces'. Ability to operate in such environments may be
essential for
many applications such as search and rescue and identification
of gas leaks in industrial settings.
-
A novel, non-reactive method for odour localisation within this domain
–
modelling the airflow in the environment (using Naïve
Physics), and then using
this map to reason about odour dispersal, and plan movements that will
provide
sensor reading that can be used to predict the location of the odour
source.
-
Development
of bi-modal search with complementary senses (vision/olfaction) for the
localisation of a chemical source. This technique could be applied
naturally to many other sensor guided tasks.
-
A
framework for improving the robustness of a
Naïve Physics algorithm. Bayesian reasoning on a multiple
airflow map hypothesis
tree was used to provide robustness against uncertainties in sensor
readings, a
priori information and errors in the airflow mapping process.
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