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Grasp recognition by fuzzy modeling and hidden Markov models
Örebro University, School of Science and Technology. (AASS)
Örebro University, School of Science and Technology. (AASS)
2010 (English)In: Robot intelligence: an advanced knowledge processing approach / [ed] Honghai Liu, Dongbing Gu, Robert J. Howlett, Yonghuai Liu, New York: Springer , 2010, p. 25-47Chapter in book (Other academic)
Abstract [en]

Grasp recognition is a major part of the approach for Programming-by-Demonstration (PbD) for five-fingered robotic hands. This chapter describes three different methods for grasp recognition for a human hand. A human operator wearing a data glove instructs the robot to perform different grasps. For a number of human grasps the finger joint angle trajectories are recorded and modeled by fuzzy clustering and Takagi-Sugeno modeling. This leads to grasp models using time as input parameter and joint angles as outputs. Given a test grasp by the human operator the robot classifies and recognizes the grasp and generates the corresponding robot grasp. Three methods for grasp recognition are compared with each other. In the first method, the test grasp is compared with model grasps using the difference between the model outputs. The second method deals with qualitative fuzzy models which used for recognition and classification. The third method is based on Hidden-Markov-Models (HMM) which are commonly used in robot learning.

Place, publisher, year, edition, pages
New York: Springer , 2010. p. 25-47
National Category
Control Engineering
Research subject
Automatic Control
Identifiers
URN: urn:nbn:se:oru:diva-14336DOI: 10.1007/978-1-84996-329-9_2ISBN: 978-1-84996-328-2 (print)OAI: oai:DiVA.org:oru-14336DiVA, id: diva2:392543
Available from: 2011-01-27 Created: 2011-01-27 Last updated: 2017-10-18Bibliographically approved

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Palm, RainerIliev, BoykoKadmiry, Bourhane

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CiteExportLink to record
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Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
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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