(Georgia Institute of Technology, 2012-07)
Quispe, Ana Huamán; Stilman, Mike
We present a novel algorithm that generates a set of diverse workspace paths for
manipulators. By considering more than one possible path we give our manipulator
the flexibility to choose from many possible ways to execute a task. This is
particularly important in cases in which the best workspace path cannot be executed
by the manipulator (e.g. due to the presence of obstacles that collide with
the manipulator links). Our workspace paths are generated such that a distance
metric between them is maximized, allowing them to span different workspace regions.
Manipulator planners mostly focus on solving the problem by analyzing the
configuration space (e.g. Jacobian-based methods); our approach focuses on analyzing
alternative workspace paths which are comparable to the optimal solution in
terms of length. This paper introduces our intuitive algorithm and also presents the
results of a series of experiments performed with a simulated 7 DOF robotic arm.
(Georgia Institute of Technology, 2012)
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 Histograms
(VFH) which classify unoccluded objects to also classifying
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 full
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)
Dantam, Neil; Essa, Irfan; Stilman, Mike
We describe several algorithms used for the inference of linguistic robot policies from human
demonstration. First, tracking and match objects using the Hungarian Algorithm. Then, we convert Regular Expressions to Nondeterministic Finite Automata (NFA) using the McNaughton-Yamada-Thompson
Algorithm. Next, we use Subset Construction to convert to a Deterministic Finite Automaton. Finally, we
minimize finite automata using either Hopcroft's Algorithm or Brzozowski's Algorithm.