Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
A Framework for Optimal Grasp Contact Planning
KTH, School of Computer Science and Communication (CSC), Robotics, perception and learning, RPL.ORCID iD: 0000-0003-4132-1217
KTH, School of Computer Science and Communication (CSC), Robotics, perception and learning, RPL.
KTH, School of Computer Science and Communication (CSC), Robotics, perception and learning, RPL.ORCID iD: 0000-0003-2965-2953
2017 (English)In: IEEE Robotics and Automation Letters, ISSN 2377-3766, E-ISSN 1949-3045, Vol. 2, no 2, 704-711 p.Article in journal (Refereed) Published
Abstract [en]

We consider the problem of finding grasp contacts that are optimal under a given grasp quality function on arbitrary objects. Our approach formulates a framework for contact-level grasping as a path finding problem in the space of supercontact grasps. The initial supercontact grasp contains all grasps and in each step along a path grasps are removed. For this, we introduce and formally characterize search space structure and cost functions underwhich minimal cost paths correspond to optimal grasps. Our formulation avoids expensive exhaustive search and reduces computational cost by several orders of magnitude. We present admissible heuristic functions and exploit approximate heuristic search to further reduce the computational cost while maintaining bounded suboptimality for resulting grasps. We exemplify our formulation with point-contact grasping for which we define domain specific heuristics and demonstrate optimality and bounded suboptimality by comparing against exhaustive and uniform cost search on example objects. Furthermore, we explain how to restrict the search graph to satisfy grasp constraints for modeling hand kinematics. We also analyze our algorithm empirically in terms of created and visited search states and resultant effective branching factor.

Place, publisher, year, edition, pages
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC , 2017. Vol. 2, no 2, 704-711 p.
Keyword [en]
Grasping, dexterous manipulation, multifingered hands, contact modeling
National Category
Robotics
Identifiers
URN: urn:nbn:se:kth:diva-217455DOI: 10.1109/LRA.2017.2651381ISI: 000413736600043OAI: oai:DiVA.org:kth-217455DiVA: diva2:1158022
Note

QC 20171117

Available from: 2017-11-17 Created: 2017-11-17 Last updated: 2017-11-17Bibliographically approved

Open Access in DiVA

No full text

Other links

Publisher's full text

Authority records BETA

Hang, KaiyuStork, Johannes A.Kragic, Danica

Search in DiVA

By author/editor
Hang, KaiyuStork, Johannes A.Kragic, Danica
By organisation
Robotics, perception and learning, RPL
In the same journal
IEEE Robotics and Automation Letters
Robotics

Search outside of DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 6 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf