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Ambient intelligence in self-organising assembly systems using the chemical reaction model

Ambient intelligence in self-organising assembly systems using the chemical reaction model
Ambient intelligence in self-organising assembly systems using the chemical reaction model
This article discusses self-organising assembly systems (SOAS), a type of assembly systems that (1) participate in their own design by spontaneously organising themselves in response to the arrival of a product order and (2) manage themselves during production. SOAS address the industry’s need for agile manufacturing systems to be highly responsive to market dynamics. Manufacturing systems need to be easily and rapidly changeable, but system re-engineering/reconfiguration and especially their (re-)programming are manual, work-intensive and error-prone procedures. With SOAS, we try to facilitate this by giving the systems gradually more self-* capabilities. SOAS eases the work of the SOAS designer and engineer when designing such as system for a specific product, and supports the work of the SOAS operator when supervising the system during production. SOAS represent an application domain of ambient intelligence and humanised computing which is not frequently considered, but therefore none the less important. This article explains how an SOAS produces its own design as the result of a self-organising process following the Chemical Abstract Machine (CHAM) paradigm: industrial robots self-assemble according to specific chemical rules in response to a product order. This paper reports on SOAS in general, the specification of the chemical reactions and their simulation in Maude.
1868-5137
163-184
Frei, Regina
fa00170f-356a-4a0d-8030-d137fd855880
Di Marzo Serugendo, Giovanna
573471c6-6fba-494c-a440-b9fdd095d1d5
Serbanuta, Traian Florin
8e740a84-d010-4db8-92ac-21073229de50
Frei, Regina
fa00170f-356a-4a0d-8030-d137fd855880
Di Marzo Serugendo, Giovanna
573471c6-6fba-494c-a440-b9fdd095d1d5
Serbanuta, Traian Florin
8e740a84-d010-4db8-92ac-21073229de50

Frei, Regina, Di Marzo Serugendo, Giovanna and Serbanuta, Traian Florin (2010) Ambient intelligence in self-organising assembly systems using the chemical reaction model. Journal of Ambient Intelligence and Humanized Computing, 1 (3), 163-184. (doi:10.1007/s12652-010-0016-0).

Record type: Article

Abstract

This article discusses self-organising assembly systems (SOAS), a type of assembly systems that (1) participate in their own design by spontaneously organising themselves in response to the arrival of a product order and (2) manage themselves during production. SOAS address the industry’s need for agile manufacturing systems to be highly responsive to market dynamics. Manufacturing systems need to be easily and rapidly changeable, but system re-engineering/reconfiguration and especially their (re-)programming are manual, work-intensive and error-prone procedures. With SOAS, we try to facilitate this by giving the systems gradually more self-* capabilities. SOAS eases the work of the SOAS designer and engineer when designing such as system for a specific product, and supports the work of the SOAS operator when supervising the system during production. SOAS represent an application domain of ambient intelligence and humanised computing which is not frequently considered, but therefore none the less important. This article explains how an SOAS produces its own design as the result of a self-organising process following the Chemical Abstract Machine (CHAM) paradigm: industrial robots self-assemble according to specific chemical rules in response to a product order. This paper reports on SOAS in general, the specification of the chemical reactions and their simulation in Maude.

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More information

e-pub ahead of print date: 16 June 2010
Published date: 1 September 2010

Identifiers

Local EPrints ID: 433103
URI: http://eprints.soton.ac.uk/id/eprint/433103
ISSN: 1868-5137
PURE UUID: 86ce04b1-fdcc-4acf-adda-68794aaa635d
ORCID for Regina Frei: ORCID iD orcid.org/0000-0002-0953-6413

Catalogue record

Date deposited: 08 Aug 2019 16:30
Last modified: 09 Jan 2022 04:07

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Contributors

Author: Regina Frei ORCID iD
Author: Giovanna Di Marzo Serugendo
Author: Traian Florin Serbanuta

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