IEEE Robotics & Automation Magazine - June 2012 - 54
*
solver for 10 DoF is
queried to generate a feasible target configuration.
This step, together with
essential for finding a
filtering
the
grasps
according to their reachfeasible grasp since a
ability, took 32 ms on
average. Because of the
stable grasp may only be
simple scene, the motion
planning itself could be
found for a small amount
performed within 75 ms
on average, resulting in
of all possible approach
an overall planning time
of 2,834 ms. The IK-solvdirections.
step can be omitted
* ing
with the IK-RRT approach, since, here, IK
solutions are sampled during motion planning. Nevertheless, a set of grasps has to be generated in advance similar
to the RRT approach. Overall, planning with the IK-RRT
algorithm took 2,907 ms on average. The Grasp-RRT planner is able to compute a collision-free grasping motion,
without the need of precomputing a set of feasible grasps,
in 339 ms on average. Rows 3-6 of Table 2 show the results
when an additional obstacle is considered [see Figure
14(d)]. As a result of the limited workspace, 200 grasps
have to be planned for the RRT and IK-RRT approaches to
serve a dense set of grasps that can be used for reliable IK
solving. Hence, the overall planning time increased to
more than 10 s for the RRT and IK-RRT algorithms. The
Grasp-RRT planner was able to solve the problem in 4.2 s
on average.
As shown in Table 2, the Grasp-RRT planner performs
well when no grasping information is present. In case offline-generated grasping data is stored in a database, the
proposed approach introduces some overhead, which is
caused by the online generation of grasping hypotheses.
Furthermore, the Grasp-RRT approach is a single-directional planner, which is known to be slower compared with
bidirectional approaches (such as RRT and IK-RRT).
Nevertheless, the Grasp-RRT algorithm is able to find
suitable solutions quickly while having the advantage that
no IK solver, no reachability information, and no dedicated grasp planner must be present for the robot.
The approach direction is
(a)
(b)
(c)
(d)
Figure 14. (a), (b) A 3-D model that was generated with shape-retrieval algorithms [24]. The start configuration was used to
compare the results of different approaches. (c), (d) Two visualizations of the results of the Grasp-RRT approach. The RRT is
visualized in a workspace, whereas the generated approach movements are shown in red and the collision-free grasping motion and
its pruned version are depicted in blue and green.
*
Table 2. A comparison of the different approaches.
Total
Grasp Planning
IK-Solving
Motion Planning
RRT
2,834 ms
2,727 ms
32 ms
75 ms
IK-RRT
2,907 ms
2,727 ms
-
180 ms
Grasp-RRT
339 ms
-
-
339 ms
RRT
10,222 ms
9,601 ms
193 ms
428 ms
IK-RRT
10,200 ms
9,601 ms
-
599 ms
Grasp-RRT
4,181 ms
-
-
4,181 ms
No obstacle, 50 grasps
With obstacles, 200 grasps
54
*
IEEE ROBOTICS & AUTOMATION MAGAZINE
*
JUNE 2012
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