A multi-agent system for the production control of printed circuit boards using JaCaMo and Prometheus AEOlus

Roloff, Mário Lucio; Stemmer, Marcelo Ricardo; Hübner, Jomi Fred; Schmitt, Robert; Pfeifer, Tilo; Hüttemann, Guido

doi:10.1109/indin.2014.6945514

Cited by 8 publications

(4 citation statements)

References 11 publications

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“…From the remaining frameworks for production based on multiagent systems (see [40,43] for comprehensive surveys on the topic), of particular relevance to our work are [19,51,32], where BDI-agent based frameworks are proposed that model the (manufacturing) environment as a dynamic set of 'artifacts', representing both physical entities such as resources on the shop floor, and computational entities such as web services. In [19], the product is specified 'declaratively' in terms of its machine-formable features, such as surface finish and colour, and any agent in the multiagent system can use semantic descriptions of currently available artifacts together with a declarative product specification to synthesise, on the fly, a production plan that is able to produce the product by requesting services from relevant artifacts.…”

Section: State Of the Artmentioning

confidence: 99%

Synthesising process controllers from formal models of transformable assembly systems

Silva¹,

Felli²,

Sanderson³

et al. 2019

Robotics and Computer-Integrated Manufacturing

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When producing complex and highly customisable products in low volumes (or in 'batch sizes of one'), automation of production systems is critical for competitiveness and profitability in high labour-cost economies. To facilitate batch-size-of-one production, 'topology generation', 'realisability', and 'control' algorithms have been developed as part of the Evolvable Assembly Systems (EAS) project. The topology generation algorithm computes all the possible sequences of parallel activities that assembly resources can perform on parts and is run offline whenever the layout of the production facility changes, whereas realisability checking and controller generation are performed at run-time to check whether a production facility with a given set of assembly resources can assemble a desired product, and how the product should be assembled, e.g., which resources to use, and when. Generated controllers are output in Business to Manufacturing Markup Language (B2MML). Taken together, the algorithms thus represent a step toward a complete path from the formal specification of an assembly system and the products to be assembled, to the automated synthesis of executable process plans. This paper presents each algorithm in sufficient detail to allow their reimplementation by other researchers. Topology generation is the most expensive step in the approach. A preliminary experimental evaluation of the scalability of topology generation is presented, which suggests that, for small to medium sized production facilities, the time required for recomputing the topology is sufficiently small not to preclude frequent factory transformations, e.g., the addition of new resources.

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Section: State Of the Artmentioning

confidence: 99%

Synthesising process controllers from formal models of transformable assembly systems

Silva¹,

Felli²,

Sanderson³

et al. 2019

Robotics and Computer-Integrated Manufacturing

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“…As far as we know, the only study that made use of A&A concept to build MAS for industrial application in Industry 4.0 context was proposed by Roloff et al (2014). The limitation of this study refers to the integration by a unique API for OPC communication.…”

Section: Related Workmentioning

confidence: 99%

“…However, MAS applications are becoming very complex and computationally heavy specially due indiscriminately agentification of entities, i.e., the approach that model almost any entity of a system as an agent. In the other hand, in recent MAS research (Hübner et al, 2010;Omicini et al, 2008;Ricci et al, 2006;Roloff et al, 2014), dynamic and complex scenarios are being analysed in dimensions, it interprets that some elements are not necessarily agents. Agents and Artifacts approach (A&A) is proposing: (i) agent's dimension for proactive entities which encapsulate autonomous execution in some activities (ii) environment dimension which includes artifacts, i.e., simpler entities that can be manipulated and shared by agents.…”

Section: Introductionmentioning

confidence: 99%

Integrating Industrial Artifacts and Agents Through Apache Camel

Amaral

Cranefield

Hübner

et al. 2019

Anais Do 14º Simpósio Brasileiro De Automação Inteligente

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There are many challenges for building up the smart factory, among them to deal with distributed data, high volume of information, and wide diversity of devices and applications. In this sense, Cyber-Physical System (CPS) concept emerges to virtualize and integrate factory resources. Based on studies that use Multi-Agent System as the core of a CPS, in this paper, we show that many resources of the factories can be modelled following the well-known Agents and Artifacts method of integrating agents and their environment. To enhance the interoperability of this system, we use Apache Camel framework, a middleware to define routes allowing the integration with a wide range of endpoints using different protocols. Finally, we present a Camel component for artifacts, designed in this research, illustrating its use.

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“…[72], [78] [18] [63] Framework JaCaMo [19] [95] [87], [84], [30] [90] [76,77] ROMAS- [39], [34] [36], [ [8] Electricity market; this example also shows how services may be attached to an electronic institution.…”

Section: Ei/eidementioning

confidence: 99%

Application Domains

Padget

Aldewereld

Noriega

et al. 2016

Social Coordination Frameworks for Social Technical Systems

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The subject of this chapter is the application of models for social coordination (M4SC). Deriving from and extending multiagent systems (MAS), applications of M4SC inherit the attributes of MAS, such as multiple actors, distribution, and heterogeneity, but bring characteristics of their own that are essential to social coordination over and above conventional MAS. In this chapter, we first discuss these differentiating properties of M4SC, identifying essential and nonessential (but commonly occurring) characteristics. This we follow with a wideranging (but not exhaustive or exclusive) discussion of classes of M4SC applications, which are supported by structured, illustrative summaries of noteworthy examples of applications, created using the frameworks described elsewhere in this volume. Finally, we give an overview of a broader range applications developed with the frameworks, with links to further reading.

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A multi-agent system for the production control of printed circuit boards using JaCaMo and Prometheus AEOlus

Cited by 8 publications

References 11 publications

Synthesising process controllers from formal models of transformable assembly systems

Synthesising process controllers from formal models of transformable assembly systems

Integrating Industrial Artifacts and Agents Through Apache Camel

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