(Georgia Institute of Technology, 2003)
Balch, Tucker; Dellaert, Frank; Khan, Zia
We describe a multiple hypothesis particle filter
for tracking targets that will be influenced by the proximity
and/or behavior of other targets. Our contribution is to show
how a Markov random field motion prior, built on the fly
at each time step, can model these interactions to enable
more accurate tracking. We present results for a social insect
tracking application, where we model the domain knowledge
that two targets cannot occupy the same space, and targets
will actively avoid collisions. We show that using this model
improves track quality and efficiency. Unfortunately, the
joint particle tracker we propose suffers from exponential
complexity in the number of tracked targets. An approximation
to the joint filter, however, consisting of multiple
nearly independent particle filters can provide similar track
quality at substantially lower computational cost.
(Georgia Institute of Technology, 2002)
Alegre, Fernando; Balch, Tucker; Berhault, Marc; Dellaert, Frank; Kaess, Michael; McGuire, Robert; Merrill, Ernest; Moshkina, Lilia; Ravichandran, Ram; Walker, Daniel
We describe our entry in the AAAI 2002 Urban Search and
Rescue (USAR) competition, a marsupial team consisting of
a larger wheeled robot and several small legged robots, carried
around by the larger robot. This setup exploits complimentary
strengths of each robot type in a challenging domain.
We describe both the hardware and software architecture, and
the on-board real-time mapping which forms the basis of accurate
victim-localization crucial to the USAR domain. We
also evaluate what challenges remain to be resolved in order
to deploy search and rescue robots in realistic scenarios.