Advanced Search

Journal Navigation

Journal Home

Subscriptions

Archive

Contact Us

Table of Contents

Click here to sign up for SAGE Journal Email Alerts today!

Sign In to gain access to subscriptions and/or personal tools.
The International Journal of Robotics Research
This Article
Right arrow Full Text (PDF)
Right arrow References
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Alert me to new issues of the journal
Right arrow Add to Saved Citations
Right arrow Download to citation manager
Right arrowRequest Permissions
Right arrow Request Reprints
Right arrow Add to My Marked Citations
Citing Articles
Right arrow Citing Articles via Google Scholar
Right arrow Citing Articles via Scopus
Google Scholar
Right arrow Articles by DeMers, D.
Right arrow Articles by Kreutz-Delgado, K.
Right arrow Search for Related Content
Social Bookmarking
 Add to CiteULike   Add to Complore   Add to Connotea   Add to Del.icio.us   Add to Digg   Add to Reddit   Add to Technorati   Add to Twitter  
What's this?

Learning Global Properties of Nonredundant Kinematic Mappings

David DeMers

Prediction Company Santa Fe, NM 87501, USA

Kenneth Kreutz-Delgado

Department of Electrical and Computer Engineering University of California, San Diego La Jolla, CA 92093, USA

The kinematic mapping x = f({theta}) is generally many to one. For nonredundant manipulators, this means that there are a finite num ber of configurations (joint angles) that will place the end-effector at a target location in the workspace. These correspond to pos tures of the manipulator, and each configuration lies on a specific solution branch. It is shown that for certain classes of revolute joint regional manipulators (those with no joint limits and having almost everywhere a constant number of inverse solutions in the workspace), the input-output data can be analyzed by clustering methods in order to determine the number and location of the so lution branches. As a practical consequence, the inverse kinematic mapping can be directly approximated by applying neural network or other learning-based methods to each branch separately.

The International Journal of Robotics Research, Vol. 17, No. 5, 547-560 (1998)
DOI: 10.1177/027836499801700506


Add to CiteULike CiteULike   Add to Complore Complore   Add to Connotea Connotea   Add to Del.icio.us Del.icio.us   Add to Digg Digg   Add to Reddit Reddit   Add to Technorati Technorati   Add to Twitter Twitter    What's this?