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Knowledge and Skill Representations for Robotized Production

Björkelund, Anders; Malec, Jacek LU ; Nilsson, Klas LU and Nugues, Pierre LU (2011) 18th IFAC World Congress, 2011 In Proceedings of the 18th IFAC World Congress, 2011 p.8999-9004
Abstract
Model-based systems in control are a means to utilize efficiently human knowledge and achieve high performance. While models consisting of formalized knowledge are used during the engineering step, running systems usually do not contain a high-level, symbolic representation of the control and most of its properties, typically named numerical parameters. On a system level and beyond the plant data, there is also a need to represent the meaning of the data such that deployment and fault analysis could be augmented with partly automated inference based on the semantics of the data. To that end, we extended the formalized knowledge traditionally used in control to include the control purpose, engineering assumption, quality, involved state... (More)
Model-based systems in control are a means to utilize efficiently human knowledge and achieve high performance. While models consisting of formalized knowledge are used during the engineering step, running systems usually do not contain a high-level, symbolic representation of the control and most of its properties, typically named numerical parameters. On a system level and beyond the plant data, there is also a need to represent the meaning of the data such that deployment and fault analysis could be augmented with partly automated inference based on the semantics of the data. To that end, we extended the formalized knowledge traditionally used in control to include the control purpose, engineering assumption, quality, involved state machines, and so on. We then represented the control semantics in a format that allows an easier extraction of information using querying and reasoning. It aims at making knowledge in control engineering reusable so that it can be shipped together with the control systems. We implemented prototypes that include automatic conversion of plant data from AutomationML into RDF triples, as well as the automated extraction of control properties, the conversion of parameters, and their storage in the same triple store. Although these techniques are standard within the semantic web community, we believe that our robotic prototypes for semantic control represent a novel approach. (Less)
Please use this url to cite or link to this publication:
author
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
keywords
Model-based control, Knowledge representation, System architectures, Autonomous control, Industrial robots
in
Proceedings of the 18th IFAC World Congress, 2011
pages
5 pages
publisher
IFAC
conference name
18th IFAC World Congress, 2011
external identifiers
  • scopus:84866751878
ISSN
1474-6670
ISBN
978-3-902661-93-7
DOI
10.3182/20110828-6-IT-1002.01053
language
English
LU publication?
yes
id
ef7338b4-d1dd-4705-bd0d-77abc99806d6 (old id 4679225)
date added to LUP
2014-09-25 13:48:55
date last changed
2017-06-04 04:12:22
@inproceedings{ef7338b4-d1dd-4705-bd0d-77abc99806d6,
  abstract     = {Model-based systems in control are a means to utilize efficiently human knowledge and achieve high performance. While models consisting of formalized knowledge are used during the engineering step, running systems usually do not contain a high-level, symbolic representation of the control and most of its properties, typically named numerical parameters. On a system level and beyond the plant data, there is also a need to represent the meaning of the data such that deployment and fault analysis could be augmented with partly automated inference based on the semantics of the data. To that end, we extended the formalized knowledge traditionally used in control to include the control purpose, engineering assumption, quality, involved state machines, and so on. We then represented the control semantics in a format that allows an easier extraction of information using querying and reasoning. It aims at making knowledge in control engineering reusable so that it can be shipped together with the control systems. We implemented prototypes that include automatic conversion of plant data from AutomationML into RDF triples, as well as the automated extraction of control properties, the conversion of parameters, and their storage in the same triple store. Although these techniques are standard within the semantic web community, we believe that our robotic prototypes for semantic control represent a novel approach.},
  author       = {Björkelund, Anders and Malec, Jacek and Nilsson, Klas and Nugues, Pierre},
  booktitle    = {Proceedings of the 18th IFAC World Congress, 2011},
  isbn         = {978-3-902661-93-7},
  issn         = {1474-6670},
  keyword      = {Model-based control,Knowledge representation,System architectures,Autonomous control,Industrial robots},
  language     = {eng},
  pages        = {8999--9004},
  publisher    = {IFAC},
  title        = {Knowledge and Skill Representations for Robotized Production},
  url          = {http://dx.doi.org/10.3182/20110828-6-IT-1002.01053},
  year         = {2011},
}