Path planning for grasping operations using an adaptive PCA-based sampling method
Visualitza/Obre
Cita com:
hdl:2117/20339
Tipus de documentArticle
Data publicació2013-07-01
Condicions d'accésAccés obert
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Abstract
The planning of collision-free paths for a handarm
robotic system is a difficult issue due to the large number
of degrees of freedom involved and the cluttered environment
usually encountered near grasping configurations. To cope
with this problem, this paper presents a novel importance
sampling method based on the use of principal component
analysis (PCA) to enlarge the probability of finding collisionfree
samples in these difficult regions of the configuration
space with low clearance. By using collision-free samples
near the goal, PCA is periodically applied in order to obtain
a sampling volume near the goal that better covers the free
space, improving the efficiency of sampling-based path planning
methods. The approach has been tested with success on
a hand-arm robotic system composed of a four-finger anthropomorphic
mechanical hand (17 joints with 13 independent
degrees of freedom) and an industrial robot (6 independent
degrees of freedom).
CitacióRosell, J.; Suarez, R.; Perez, A. Path planning for grasping operations using an adaptive PCA-based sampling method. "Autonomous robots", 01 Juliol 2013, vol. 35, núm. 1, p. 27-36.
ISSN0929-5593
Versió de l'editorhttp://link.springer.com/article/10.1007%2Fs10514-013-9332-5
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