(Georgia Institute of Technology, 2013-05)
Levihn, Martin; Scholz, Jonathan; Stilman, Mike
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.
(Georgia Institute of Technology, 2013-01)
Levihn, Martin; Dutton, Matthew; Trevor, Alexander J. B.; Stilman, Mike
This paper presents a novel algorithm: Verfied Partial
Object Detector (VPOD) for accurate detection of partially occluded objects such as furniture in 3D point clouds.
VPOD is implemented and validated on real sensor data
obtained by our robot. It extends Viewpoint Feature His-
tograms (VFH), which classify unoccluded objects, to also
classify partially occluded objects such as furniture that
might be seen in typical office environments. To achieve this
result, VPOD employs two strategies. First, object models
are segmented and the object database is extended to include partial models. Second, once a matching partial object is detected, the complete object model is aligned back
into the scene and verified for consistency with the point
cloud data. Overall, our approach increases the number of
objects found and substantially reduces false positives due
to the verification process.
(Georgia Institute of Technology, 2012-10)
Levihn, Martin; Igarashi, Takeo; Stilman, Mike
We present
Assignment Space Planning, a new efficient robot multi-agent coordination algorithm for the PSPACE-
hard problem of multi-robot multi-object push rearrangement.
In both simulated and real robot experiments, we demonstrate
that our method produces optimal solutions for simple problems
and exhibits novel emergent behaviors for complex scenarios.
Assignment Space
takes advantage of the domain structure by
splitting the planning up into three stages, effectively reducing
the search space size and enabling the planner to produce
optimized plans in seconds. Our algorithm finds solutions of
comparable quality to complete configuration space search
while reducing the computing time to seconds, which allows
our approach to be applied in practical scenarios in real-time.