Planning with Movable Obstacles in Continuous Environments with Uncertain Dynamics
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Author(s)
Levihn, Martin
Scholz, Jonathan
Stilman, Mike
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Abstract
In this paper we present a decision theoretic
planner for the problem of Navigation Among Movable Obstacles (NAMO) operating under conditions faced by real
robotic systems. While planners for the NAMO domain exist,
they typically assume a deterministic environment or rely on
discretization of the configuration and action spaces, preventing
their use in practice. In contrast, we propose a planner that
operates in real-world conditions such as uncertainty about the
parameters of workspace objects and continuous configuration
and action (control) spaces.
To achieve robust NAMO planning despite these conditions,
we introduce a novel integration of Monte Carlo simulation
with an abstract MDP construction. We present theoretical and
empirical arguments for time complexity linear in the number
of obstacles as well as a detailed implementation and examples
from a dynamic simulation environment.
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Date
2013-05
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