Title:
Probabilistic Planning for Behavior-Based Robots
Probabilistic Planning for Behavior-Based Robots
Author(s)
Atrash, Amin
Koenig, Sven
Koenig, Sven
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Abstract
Partially Observable Markov Decision Process models
(POMDPs) have been applied to low-level robot control. We
show how to use POMDPs differently, namely for sensor-planning
in the context of behavior-based robot systems. This
is possible because solutions of POMDPs can be expressed as
policy graphs, which are similar to the finite state automata
that behavior-based systems use to sequence their behaviors.
An advantage of our system over previous POMDP navigation
systems is that it is able to find close-to-optimal plans
since it plans at a higher level and thus with smaller state
spaces. An advantage of our system over behavior-based systems
that need to get programmed by their users is that it can
optimize plans during missions and thus deal robustly with
probabilistic models that are initially inaccurate.
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Date Issued
2001
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Text
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Paper