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Random field estimation approach to robot dynamicsThe difference equations of Kalman filtering and smoothing recursively factor and invert the covariance of the output of a linear state-space system driven by a white-noise process. Here it is shown that similar recursive techniques factor and invert the inertia matrix of a multibody robot system. The random field models are based on the assumption that all of the inertial (D'Alembert) forces in the system are represented by a spatially distributed white-noise model. They are easier to describe than the models based on classical mechanics, which typically require extensive derivation and manipulation of equations of motion for complex mechanical systems. With the spatially random models, more primitive locally specified computations result in a global collective system behavior equivalent to that obtained with deterministic models. The primary goal of applying random field estimation is to provide a concise analytical foundation for solving robot control and motion planning problems.
Document ID
19910032826
Acquisition Source
Legacy CDMS
Document Type
Reprint (Version printed in journal)
External Source(s)
Authors
Rodriguez, Guillermo
(JPL Pasadena, CA, United States)
Date Acquired
August 15, 2013
Publication Date
October 1, 1990
Publication Information
Publication: IEEE Transactions on Systems, Man, and Cybernetics
Volume: 20
ISSN: 0018-9472
Subject Category
Cybernetics
Accession Number
91A17449
Distribution Limits
Public
Copyright
Other

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