Path planning for grasping operations using an adaptive PCA-based sampling method
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 met...
| Autores: | , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2013 |
| País: | España |
| Institución: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2117/20339 |
| Acceso en línea: | https://hdl.handle.net/2117/20339 https://dx.doi.org/10.1007/s10514-013-9332-5 |
| Access Level: | acceso abierto |
| Palabra clave: | Robot hands Anthropomorphic hands Grasping Importance sampling Motion planning Principal component analysis Mans mecàniques Àrees temàtiques de la UPC::Informàtica::Robòtica |
| Sumario: | 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). |
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