Planning Under Uncertainty in the Continuous Domain: A Generalized Belief Space Approach

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Date
2014Author
Indelman, Vadim
Carlone, Luca
Dellaert, Frank
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This work investigates the problem of planning
under uncertainty, with application to mobile robotics. We
propose a probabilistic framework in which the robot bases
its decisions on the
generalized belief
, which is a probabilistic
description of its own state and of external variables of interest.
The approach naturally leads to a dual-layer architecture: an
inner estimation layer, which performs inference to predict the
outcome of possible decisions, and an
outer decisional layer
which is in charge of deciding the best action to undertake.
The approach does not discretize the state or control space,
and allows planning in continuous domain. Moreover, it allows
to relax the assumption of
maximum likelihood observations: predicted measurements are treated as random variables and
are not considered as
given. Experimental results show that
our planning approach produces smooth trajectories while
maintaining uncertainty within reasonable bounds.