Candidate:
Shamak
Dutta
Title:
Informative
Sampling
&
Path
Planning
in
Random
Fields
Date:
September
12,
2022
Time:
10:00
AM
Place:
REMOTE
PARTICIPATION
Supervisor:
Smith,
Stephen
L.
Abstract:
In
this
talk,
I
will
discuss
some
recent
results
in
sampling
and
path
planning
for
robots
in
random
fields.
First,
I
will
discuss
the
subset
selection
problem
in
a
spatial
field
where
we
seek
to
find
a
set
of
'k'
locations
whose
observations
provide
the
best
estimate
of
the
field
value
over
a
finite
set
of
prediction
locations.
We
propose
a
greedy
algorithm
that
searches
only
over
the
prediction
locations
and
the
centroids
of
cliques
formed
by
the
prediction
locations.
We
demonstrate
the
effectiveness
of
this
approach,
in
terms
of
solution
quality
and
runtime,
over
a
greedy
algorithm
that
searches
over
a
grid
discretization,
which
is
a
popular
approach
to
solve
the
problem.
Next,
we
present
a
new
mixed
integer
formulation
for
the
discrete
informative
path
planning
problem
in
random
fields.
The
objective
is
to
compute
a
budget-constrained
path
while
collecting
measurements
whose
linear
estimate
results
in
minimum
error
over
a
finite
set
of
prediction
locations.
Our
approach
expands
the
search
space
so
that
we
optimize
not
only
over
the
measurement
subset
but
also
over
the
class
of
all
linear
estimators.
This
allows
us
to
formulate
the
problem
as
a
mixed-integer
quadratic
program
whose
objective
is
convex
in
the
continuous
variables.
In
simulations,
we
demonstrate
the
benefit
of
our
approach
over
previous
branch
and
bound
algorithms.
Finally,
I
will
conclude
with
the
work
I
am
currently
focusing
on
relating
to
adaptive
sampling
in
random
fields,
where
the
covariance
structure
is
unknown.
Monday, September 12, 2022 10:00 am
-
10:00 am
EDT (GMT -04:00)