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David DeMers

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5 papers

2 author rows

ICRA Conference 1994 Conference Paper

Canonically Parameterized Families of Inverse Kinematic Functions for Redundant Manipulators

David DeMers
Kenneth Kreutz-Delgado

The workspace of a redundant manipulator can be partitioned into a finite number of invertible subsets such that the inverse image of each subset has trivial fiber bundle topology, with the manifolds of manipulator self-motion as the fibers. As a consequence we show that a canonical representation of the self-motion manifolds is possible over these well-defined subsets. The canonical representation parameterizes a family of inverse functions over the work space subsets. This parameterization can be used to approximate a continuous inverse kinematic function over each workspace subset. Setting appropriate valves of the parameters yields particular inverse functions which provide access to all solution branches and to the full range of the redundant degrees of freedom in order to satisfy any side constraints which may be imposed during operation. >

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NeurIPS Conference 1992 Conference Paper

Global Regularization of Inverse Kinematics for Redundant Manipulators

David DeMers
Kenneth Kreutz-Delgado

The inverse kinematics problem for redundant manipulators is ill-posed and nonlinear. There are two fundamentally different issues which result in the need for some form of regularization; the existence of multiple solution branches (global ill-posedness) and the existence of excess degrees of freedom (local ill(cid: 173) posedness). For certain classes of manipulators, learning methods applied to input-output data generated from the forward function can be used to globally regularize the problem by partitioning the domain of the forward mapping into a finite set of regions over which the inverse problem is well-posed. Local regularization can be accomplished by an appropriate parameterization of the redundancy consistently over each region. As a result, the ill-posed problem can be transformed into a finite set of well-posed problems. Each can then be solved separately to construct approximate direct inverse functions.

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NeurIPS Conference 1992 Conference Paper

Non-Linear Dimensionality Reduction

David DeMers
Garrison Cottrell

A method for creating a non-linear encoder-decoder for multidimensional data with compact representations is presented. The commonly used technique of autoassociation is extended to allow non-linear representations, and an objec(cid: 173) tive function which penalizes activations of individual hidden units is shown to result in minimum dimensional encodings with respect to allowable error in reconstruction.

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NeurIPS Conference 1991 Conference Paper

Learning Global Direct Inverse Kinematics

David DeMers
Kenneth Kreutz-Delgado

We introduce and demonstrate a bootstrap method for construction of an in(cid: 173) verse function for the robot kinematic mapping using only sample configuration(cid: 173) space/workspace data. Unsupervised learning (clustering) techniques are used on pre-image neighborhoods in order to learn to partition the configuration space into subsets over which the kinematic mapping is invertible. Supervised leam(cid: 173) ing is then used separately on each of the partitions to approximate the inverse function. The ill-posed inverse kinematics function is thereby regularized, and a globa1 inverse kinematics solution for the wristless Puma manipulator is devel(cid: 173) oped.

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ICRA Conference 1991 Conference Paper

Operator approach to recursive Jacobian inversion and pseudoinversion

Kenneth Kreutz-Delgado
Daryush Agahi
David DeMers

Operator forms of the inverse and Moore-Penrose pseudoinverse of the Jacobian for a nonsingular n-link serial spatial manipulator are developed. It is shown that the Jacobian pseudoinverse operation that maps an end-effector velocity to a corresponding joint-space velocity can be performed using an O(n) recursive algorithm involving one 6*6 matrix inversion. It is also shown that an alternative recursive algorithm that produces a kinematic singularity measure as a byproduct can be used to perform the Jacobian inverse operation without the need for a matrix inversion. >

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