Referenced Items are not available for this document.
An efficient first grasp for a wheelchair robotic arm-hand with pressure sensing is determined and presented. The grasp is learned by combining the advantages of neural networks and fuzzy logic into a hybrid control algorithm which learns from its tip and slip control experiences. Neurofuzzy modifications are outlined, and basic steps are demonstrated in preparation for physical implementation. Choice of object approach vector based on fuzzy tip and slip data and an expert supervisor, as well as training of a diagnostic neural tip and slip controller, are the focus of this work
Published in:
Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on
(Volume:29
,
Issue:
5
)
Date of Publication: Oct 1999
- Page(s):
- 636 - 642
- ISSN :
- 1083-4419
- INSPEC Accession Number:
- 6378367
- Digital Object Identifier :
- 10.1109/3477.790448
- Product Type:
- Journals & Magazines
- Date of Current Version :
- 06 August 2002
- Issue Date :
- Oct 1999
- Sponsored by :
- IEEE Systems, Man, and Cybernetics Society
About IEEE Xplore | Contact | Help | Terms of Use | Nondiscrimination Policy | Site Map | Privacy & Opting Out of Cookies
A not-for-profit organization, IEEE is the world's largest professional association for the advancement of technology.
© Copyright 2013 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.
