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Knowledge-Based Instruction of Manipulation Tasks for Industrial Robotics

Stenmark, Maj LU and Malec, Jacek LU (2015) In Robotics and Computer-Integrated Manufacturing 33. p.56-67
Abstract
When robots are working in dynamic environments, close to humans lacking extensive knowledge of robotics, there is a strong need to simplify the user interaction and make the system execute as autonomously as possible, as long as it is feasible. For industrial robots working side-by-side with humans in manufacturing industry, AI systems are necessary to lower the demand on programming time and system integration expertise. Only by building a system with appropriate knowledge and reasoning services can one simplify the robot programming sufficiently to meet those demands while still getting a robust and efficient task execution.

In this paper, we present a system we have realized that aims at fulfilling the above demands. The paper... (More)
When robots are working in dynamic environments, close to humans lacking extensive knowledge of robotics, there is a strong need to simplify the user interaction and make the system execute as autonomously as possible, as long as it is feasible. For industrial robots working side-by-side with humans in manufacturing industry, AI systems are necessary to lower the demand on programming time and system integration expertise. Only by building a system with appropriate knowledge and reasoning services can one simplify the robot programming sufficiently to meet those demands while still getting a robust and efficient task execution.

In this paper, we present a system we have realized that aims at fulfilling the above demands. The paper focuses on the knowledge put into ontologies created for robotic devices and manufacturing tasks, and presents examples of AI-related services that use the semantic descriptions of skills to help users instruct the robot adequately. (Less)
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author
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
knowledge representation, robot skill, industrial robotics ontology, assembly, service-oriented architecture
in
Robotics and Computer-Integrated Manufacturing
volume
33
pages
56 - 67
publisher
Elsevier
external identifiers
  • wos:000349725400007
  • scopus:84921754522
  • scopus:85027922369
ISSN
0736-5845
DOI
10.1016/j.rcim.2014.07.004
language
English
LU publication?
yes
id
b71ce47c-fc98-484f-8745-dbdf0ddf8f8c (old id 4679243)
date added to LUP
2014-09-25 14:11:28
date last changed
2017-09-25 12:35:06
@article{b71ce47c-fc98-484f-8745-dbdf0ddf8f8c,
  abstract     = {When robots are working in dynamic environments, close to humans lacking extensive knowledge of robotics, there is a strong need to simplify the user interaction and make the system execute as autonomously as possible, as long as it is feasible. For industrial robots working side-by-side with humans in manufacturing industry, AI systems are necessary to lower the demand on programming time and system integration expertise. Only by building a system with appropriate knowledge and reasoning services can one simplify the robot programming sufficiently to meet those demands while still getting a robust and efficient task execution.<br/><br>
In this paper, we present a system we have realized that aims at fulfilling the above demands. The paper focuses on the knowledge put into ontologies created for robotic devices and manufacturing tasks, and presents examples of AI-related services that use the semantic descriptions of skills to help users instruct the robot adequately.},
  author       = {Stenmark, Maj and Malec, Jacek},
  issn         = {0736-5845},
  keyword      = {knowledge representation,robot skill,industrial robotics ontology,assembly,service-oriented architecture},
  language     = {eng},
  pages        = {56--67},
  publisher    = {Elsevier},
  series       = {Robotics and Computer-Integrated Manufacturing},
  title        = {Knowledge-Based Instruction of Manipulation Tasks for Industrial Robotics},
  url          = {http://dx.doi.org/10.1016/j.rcim.2014.07.004},
  volume       = {33},
  year         = {2015},
}