(Georgia Institute of Technology, 2005-04)
Balch, Tucker; Dellaert, Frank; Delmotte, Florent; Khan, Zia; Egerstedt, Magnus B.
In this paper, we study the problem of going
from a real-world, multi-agent system to the generation of
control programs in an automatic fashion. In particular,
a computer vision system is presented, capable of simultaneously
tracking multiple agents, such as social insects.
Moreover, the data obtained from this system is fed into a
mode-reconstruction module that generates low-complexity
control programs, i.e. strings of symbolic descriptions of
control-interrupt pairs, consistent with the empirical data.
The result is a mechanism for going from the real system to
an executable implementation that can be used for controlling
multiple mobile robots.
(Georgia Institute of Technology, 2003)
Khan, Zia; Balch, Tucker; Dellaert, Frank
We describe a Markov chain Monte Carlo based particle filter that effectively deals with interacting targets, i.e., targets that are influenced by the proximity and/or behavior of other targets. Such interactions cause problems for traditional approaches to the data association problem. In response, we developed a joint tracker that includes a more sophisticated motion model to maintain the identity of targets throughout an interaction, drastically reducing tracker failures. The paper presents two main contributions: (1) we show how a Markov random field (MRF) motion prior, built on the fly at each time step, can substantially improve tracking when targets interact, and (2) we show how this can be done efficiently using Markov chain Monte Carlo (MCMC) sampling. We prove that incorporating an MRF to model interactions is equivalent to adding an additional interaction factor to the importance weights in a joint particle filter. Since a joint particle filter suffers from exponential complexity in the number of tracked targets, we replace the traditional importance sampling step in the particle filter with an MCMC sampling step. The resulting filter deals efficiently and effectively with complicated interactions when targets approach each other. We present both qualitative and quantitative results to substantiate the claims made in the paper, including a large scale experiment on a video-sequence of over 10,000 frames in length.
(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.