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@MastersThesis{lupu-msc,
author = {Ilan Lupu},
title = {Optimal Construction of Control Graphs in Multi-Robot Systems},
school = {{B}ar {I}lan {U}niversity},
year = {2015},
OPTkey = {},
OPTtype = {},
OPTaddress = {},
OPTmonth = {},
OPTnote = {Available at \url{http://www.cs.biu.ac.il/~galk/Publications/b2hd-lupu-msc.html}},
OPTannote = {},
wwwnote = {},
abstract = {Control graphs are used in multi-robot systems to maintain information
about which robot senses another robot, and at what position. On the basis of such graphs,
it is possible to compute a shared coordinate system, localize relative to others,
and maintain stable formations. While existing work shows how to utilize control graphs for
these tasks, it makes two critical assumptions. First, it assumes edge
weights of control graphs are single deterministic scalars. However in
reality there are many stochastic factors (e.g, latency, resource costs,
or position errors), that affect optimality of control graphs. Second,
it assumes that a single robot is given, to serve as the global anchor
for the robotsâ€™ relative positioning and location estimates. However,
optimal selection of this robot is an open problem. In this work, we
generalize control graphs to distinguish different stochastic sensing factors
that may be represented by control graphs, beyond existing work,
and discuss risk-based policies for their treatment. We show that existing
work in coordinate frame alignment and formation maintenance
may be recast as graph-theoretic algorithms inducing control graphs
for more general representation of the sensing capabilities of robots.
We then formulate the problem of optimal selection of an anchor, and
present a centralized algorithm for solving it. We evaluate use of these
algorithm on physical and simulated robots equipped with depth and
image sensors (RGB-D cameras), and show they very significantly improve
on existing work. },
}