DMCA
Canonical Parameterization of Excess Motor Degrees of Freedom with Self-Organizing Maps (1994)
| Venue: | IEEE Trans. Neural Networks |
| Citations: | 13 - 2 self |
BibTeX
@ARTICLE{Demers94canonicalparameterization,
author = {David Demers and Kenneth Kreutz-delgado},
title = {Canonical Parameterization of Excess Motor Degrees of Freedom with Self-Organizing Maps},
journal = {IEEE Trans. Neural Networks},
year = {1994},
volume = {7}
}
Share
 |
 |
 |
 |
||
OpenURL
Abstract
The problem of sensorimotor control is underdetermined due to excess (or "redundant") degrees of freedom when there are more joint variables than the minimum needed for positioning an end--effector. A method is presented for solving the nonlinear inverse kinematics problem for a redundant manipulator by learning a natural parameterization of the inverse solution manifolds with self--organizing maps. The parameterization approximates the topological structure of the joint space, which is that of a fiber bundle. The fibers represent the "self--motion manifolds" along which the manipulator can change configuration while keeping the end--effector at a fixed location. The method is demonstrated for the case of the redundant planar manipulator. Data samples along the self--motion manifolds are selected from a large set of measured input--output data. This is done by taking points in the joint space corresponding to end-effector locations near "query points", which define small neighborhoods ...
Keyphrases
excess motor degree canonical parameterization self-organizing map end effector joint space self motion manifold topological structure redundant manipulator fixed location inverse solution manifold natural parameterization data sample measured input output data large set fiber bundle small neighborhood end-effector location query point nonlinear inverse kinematics problem redundant planar manipulator joint variable sensorimotor control
