The efficient computation of uncertainty spaces for sensor-based robot planning

R. H. Taylor; V. T. Rajan

doi:10.1109/IROS.1988.593281

The efficient computation of uncertainty spaces for sensor-based robot planning

R. H. Taylor, V. T. Rajan

Research output: Contribution to journal › Conference article › peer-review

6 Scopus citations

Abstract

This paper describes methods for propogating systems of constrained variables, which may represent geometric uncertainties, sensing errors, disturbance forces, or other variations, through equations describing coordinate transformations in the task domain and for projecting the resulting large linear system onto a lower-dimensional space representing specific variations of interest for a particular problem. We have implemented a system based on these methods. We describe the mathematical representation, briefly describe two projection algorithms; and present a number of examples applying our implementation to robot task planning problems.

Original language	English (US)
Article number	593281
Pages (from-to)	231-236
Number of pages	6
Journal	IEEE International Conference on Intelligent Robots and Systems
Volume	1988-October
DOIs	https://doi.org/10.1109/IROS.1988.593281
State	Published - 1988
Externally published	Yes
Event	1988 IEEE International Workshop on Intelligent Robots and Systems: Toward the Next Generation Robot and System, IROS 1988 - Tokyo, Japan Duration: Oct 31 1988 → Nov 2 1988

ASJC Scopus subject areas

Control and Systems Engineering
Software
Computer Vision and Pattern Recognition
Computer Science Applications

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10.1109/IROS.1988.593281

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abstract = "This paper describes methods for propogating systems of constrained variables, which may represent geometric uncertainties, sensing errors, disturbance forces, or other variations, through equations describing coordinate transformations in the task domain and for projecting the resulting large linear system onto a lower-dimensional space representing specific variations of interest for a particular problem. We have implemented a system based on these methods. We describe the mathematical representation, briefly describe two projection algorithms; and present a number of examples applying our implementation to robot task planning problems.",

author = "Taylor, {R. H.} and Rajan, {V. T.}",

note = "Publisher Copyright: {\textcopyright} 1988 by the Institute of Electrical and Electronics Engineers, Inc. (IEEE). Copyright: Copyright 2019 Elsevier B.V., All rights reserved.; 1988 IEEE International Workshop on Intelligent Robots and Systems: Toward the Next Generation Robot and System, IROS 1988 ; Conference date: 31-10-1988 Through 02-11-1988",

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doi = "10.1109/IROS.1988.593281",

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AU - Rajan, V. T.

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N2 - This paper describes methods for propogating systems of constrained variables, which may represent geometric uncertainties, sensing errors, disturbance forces, or other variations, through equations describing coordinate transformations in the task domain and for projecting the resulting large linear system onto a lower-dimensional space representing specific variations of interest for a particular problem. We have implemented a system based on these methods. We describe the mathematical representation, briefly describe two projection algorithms; and present a number of examples applying our implementation to robot task planning problems.

AB - This paper describes methods for propogating systems of constrained variables, which may represent geometric uncertainties, sensing errors, disturbance forces, or other variations, through equations describing coordinate transformations in the task domain and for projecting the resulting large linear system onto a lower-dimensional space representing specific variations of interest for a particular problem. We have implemented a system based on these methods. We describe the mathematical representation, briefly describe two projection algorithms; and present a number of examples applying our implementation to robot task planning problems.

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JO - IEEE International Conference on Intelligent Robots and Systems

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Y2 - 31 October 1988 through 2 November 1988

ER -

The efficient computation of uncertainty spaces for sensor-based robot planning

Abstract

ASJC Scopus subject areas

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