NDLI: KD trees and Delaunay based linear interpolation for kinematic control: a comparison to neural networks with error backpropagation

Content Provider	IEEE Xplore Digital Library
Author	Gross, E.M.
Copyright Year	1995
Description	Author affiliation: Manuf. Eng. Lab., Toshiba Corp., Yokohama, Japan (Gross, E.M.)
Abstract	We illustrate how a KD tree data structure with Delaunay triangulation can be used for function learning. The example function is the inverse kinematics of a 3-DOF robot. The result can subsequently be used for kinematic control. The KD tree is used to efficiently extract a set number of nearest neighbors to a query point. Delaunay triangulation provides a good criteria for constructing a continuous linear approximation to the true function from neighborhood points of the query. For comparison purposes we solve the same problem with a neural network trained with error backpropagation. We conclude that the KD/Delaunay approach, in comparison to neural networks, can potentially yield a massive reduction in training time and significantly improve function estimate performance.
Starting Page	1485
Ending Page	1490
File Size	585717
Page Count	6
File Format	PDF
ISBN	0780319656
ISSN	10504729
DOI	10.1109/ROBOT.1995.525485
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1995-05-21
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Interpolation Kinematics Neural networks Tree data structures Robots Data mining Nearest neighbor searches Linear approximation Backpropagation Yield estimation
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering Control and Systems Engineering Artificial Intelligence Software

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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