IMPLEMENTATION OF AN ONTOLOGY-BASED APPROACH TO ENABLE AGILITY IN KIT BUILDING APPLICATIONS

Published

December 14, 2018

Author(s)

Zeid Kootbally, Thomas Kramer, Craig I. Schlenoff, Satyandra K. Gupta

Abstract

The effort described in this paper attempts to integrate agility aspects in the "Agility Performance of Robotic Systems" (APRS) project, developed at the National Institute of Standards and Technology (NIST). The technical idea for the APRS project is to develop the measurement science in the form of an integrated agility framework enabling manufacturers to assess and assure the agility performance of their robot systems. This framework includes robot agility performance metrics, information models, test methods, and protocols. This paper focuses on the information models and describes how they are used to introduce robot agility within the kitting domain. The described models have been fully defined in the XML Schema Definition Language (XSDL) and in the Web Ontology Language (OWL) for kit building applications. Kit building or kitting is a process that brings parts that will be used in assembly operations together in a kit and then moves the kit to the area where the parts are used in the final assembly. Details are given on the automatic generation of OWL class model files from XML schema model files. Files of OWL instances conforming to an OWL class model are generated automatically from XML instance files by automatically-built translators. Furthermore, a design methodology for kit building is presented and shows how the different information models are used with the other components of the methodolofy.

Citation

International Journal of Semantic Computing

Volume

12

Issue

1

Pub Type

Journals

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DOI Link

Keywords

Web Ontology Language, kit building, robotics, agility, knowledge representation, information model

Citation

Kootbally, Z. , Kramer, T. , Schlenoff, C. and Gupta, S. (2018), IMPLEMENTATION OF AN ONTOLOGY-BASED APPROACH TO ENABLE AGILITY IN KIT BUILDING APPLICATIONS, International Journal of Semantic Computing, [online], https://doi.org/10.1142/S1793351X18400019 (Accessed November 23, 2024)