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- © Robotics
Motivation
Grasping is an
essential feature to enable robots to interact with humans or
manipulate the physical envi-ronment. For humans grasping is a simple
task that does not need much eort. But when a human grasps an object,
the hand is oriented and preshaped regarding the volume of the object.
Finding this orientation and preconguration of the hand is complex
because the number of degrees of freedom of the arm and hand is high
and the model is not perceived completely.
Most current grasping
approaches need perfect geometrical models of the object and the
environmental scene. Using this knowledge the grasp is planned. Those
approaches are successful in a simulated environment where the perfect
geometrical model of the object is known. Nevertheless, those grasping
techniques fail often during real world experiments where the model of
the real world is not known precisely.
Expected
Outcome
The goal of this work is to classify
dierent objects in a given set of grasp categories to nd the
pre-grasp that is most suitable for that specic object. In addition
to this, part of the work is to nd the position and orientation of
the classied object. This allows robots to grasp dierent objects
without having knowledge about the exact shape or the function of that
object. This approach should conrm the hypotheses that no object
recognition is needed and that a coarse model is sufficient to grasp
objects successfully. This strategy aims to be more robust and dealing
with measurement noise.
Description of
Work
Our grasping approach compared with most
current ones does not rely on a perfect model. Additionally, the
computational cost for the algorithm is signicantly lower as planning
is no longer necessary. The compliance of the hand is used, hence a
coarse model that does not need to be perfect is sufficient to compute
grasps successfully.
In other words, the grasp of similar objects
of one category i.e. a tomato, an apple, or a pear of the sphere
category is grasped with the same pre-grasp (here: the spherical one).
So it is not necessary to dierentiate between above the named objects
to grasp them successfully.
The robot perceives the objects to
grasp and the environment by the measurement of a 3D sensor. This
measurements are searched for a planar surface where the objects are
posed on. All measurements above the planar surface are clustered in
groups to get for all objects a separate point cloud area. These
clusters are used to nd the perceptual primitives that yield the best
known pre-grasp for that specic object. Perceptual primitives are
characteristics which classify objects to the dierent pre-grasp
categories. The position and orientation is also obtained from these
clusters.
Thesis [2]
otos/Screenshot_1_2_3_neu-09.03.2011.jpg
heses/MS_2012_Schrandt__Stefan.pdf
heses/MS_2012_Schrandt__Stefan.pdf
_brock/parameter/en/font4/maxhilfe/