Self-organizing multiagent mechatronic systems in perspective

Ribeiro, Luís; Barata, José

doi:10.1109/indin.2013.6622916

Cited by 7 publications

(6 citation statements)

References 24 publications

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“…Nevertheless, it is worth pointing out that the traditional definitions and interpretations of CPSs are not able to capture the essence of the above circumscribed changes [13]. The main reason is that they place the emphasis on having a predefined and deterministic tight coupling between the physical world and the cyber-world, rather than on achieving synergism between run-time acquired data and dynamic operational objectives [14]. By doing so, they actually restrict the paradigmatic evolution of this family of engineered systems.…”

Section: An Evolutionary View On Cyber-physical Systemsmentioning

confidence: 99%

A Computational Framework for Procedural Abduction Done by Smart Cyber-Physical Systems

Horváth

2018

Designs

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To be able to provide appropriate services in social and human application contexts, smart cyber-physical systems (S-CPSs) need ampliative reasoning and decision-making (ARDM) mechanisms. As one option, procedural abduction (PA) is suggested for self-managing S-CPSs. PA is a knowledge-based computation and learning mechanism. The objective of this article is to provide a comprehensive description of the computational framework proposed for PA. Towards this end, first the essence of smart cyber-physical systems is discussed. Then, the main recent research results related to computational abduction and ampliative reasoning are discussed. PA facilitates beliefs-driven contemplation of the momentary performance of S-CPSs, including a ‘best option’-based setting of the servicing objective and realization of any demanded adaptation. The computational framework of PA includes eight clusters of computational activities: (i) run-time extraction of signals and data by sensing, (ii) recognition of events, (iii) inferring about existing situations, (iv) building awareness of the state and circumstances of operation, (v) devising alternative performance enhancement strategies, (vi) deciding on the best system adaptation, (vii) devising and scheduling the implied interventions, and (viii) actuating effectors and controls. Several cognitive algorithms and computational actions are used to implement PA in a compositional manner. PA necessitates not only a synergic interoperation of the algorithms, but also an objective-dependent fusion of the pre-programmed and the run time acquired chunks of knowledge. A fully fledged implementation of PA is underway, which will make verification and validation possible in the context of various smart CPSs.

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Section: An Evolutionary View On Cyber-physical Systemsmentioning

confidence: 99%

A Computational Framework for Procedural Abduction Done by Smart Cyber-Physical Systems

Horváth

2018

Designs

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“…Modern manufacturing systems, as they are idealized by modern manufacturing paradigms also consist on many 'intelligent' autonomous modular entities with social capabilities, that dynamically establish interactions with each other, in order to achieve common objectives. However, the development and implementation of innovative industrial applications which rely on bio-inspired principles tend to stumble in some important gaps between the conceptual academic models and their technological realization, as pointed out in [10]. Hence, one as to master the intricacies of biological complex systems in order to effectively translate their regulatory principles into modern modular manufacturing structures.…”

Section: Relationship To Collective Awareness Systemsmentioning

confidence: 99%

“…As stressed before, an optimal decision can only be taken when there is a general overview of all the system information. Even though, it is possible to compose a consistent global view by the exchange of local information, it is impractical and inefficient with the increasing number of entities [10]. Nevertheless, the distribution of both the knowledge and decision nodes ensures the system responsiveness and robustness to malfunctions, reducing the effect of deviations or catastrophic failures of the individuals.…”

Section: The Relevance Of Evolution and Adaptationmentioning

confidence: 99%

“…In this context, the integration of regulating bio-inspired principles, particularly bio-inspired self-organization, may be the way to unravel the potential of modern manufacturing paradigms. Even though these techniques have been fairly used in modern approaches [8], [10], when down to the implementation, bio-inspired methods tend to be replaced by negotiation-based procedures which tend to be efficiently poor. Consequently, current modern manufacturing systems architectures performance and scalability is typically constrained by the architecture's artificially devised self-organizing approaches and control structures.…”

Section: Towards Bio-inspired Complex Manufacturing Systemsmentioning

confidence: 99%

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Challenges and Properties for Bio-inspiration in Manufacturing

Ferreira

Ribeiro²,

Onori

et al. 2014

Technological Innovation for Collective Awareness Systems

Self Cite

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The increasing market fluctuations and customized products demand have dramatically changed the focus of industry towards organizational sustainability and supply chain agility. Such critical changes inevitably have a direct impact on the shop-floor operational requirements. In this sense, a number of innovative production paradigms emerged, providing the necessary theoretical background to such systems. Due to similarities between innovative modular production floors and natural complex systems, modern paradigms theoretically rely on bio-inspired concepts to attain the characteristics of biological systems. Nevertheless, during the implementation phase, bio-inspired principles tend to be left behind in favor of more traditional approaches, resulting in simple distributed systems with considerable limitations regarding scalability, reconfigurable ability and distributed problem resolution. This paper analyzes and presents a brief critical review on how bio-inspired concepts are currently being explored in the manufacturing environment, in an attempt to formulate a number of challenges and properties that need to be considered in order to implement manufacturing systems that closely follow the biological principles and consequently present overall characteristics of complex natural systems.

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“…Some of this work includes modular machine tools and material handlers (Heilala and Voho 2001;Landers et al 2001;Shirinzadeh 2002;Müller et al 2013) and distributed automation (Brennan and Norrie 2001;Vyatkin 2007;Lepuschitz et al 2010;Vallee et al 2011). Additionally, a wide set of artificially intelligent paradigms such as multi-agent systems (Shen and Norrie 1999;Shen et al 2000;Leitao 2009; Leitao and Restivo 2006;Leitao et al 2012;Ribeiro and Barata 2013;Lin et al 2013;Trappey et al 2013), and Holonic manufacturing systems (Babiceanu and Chen 2006;Marik et al 2002;McFarlane and Bussmann 2000;McFarlane et al 2003) have emerged. This work is particularly concerned with the integration of these intelligent control techniques within manufacturing systems.…”

Section: Introductionmentioning

confidence: 99%

Measures of reconfigurability and its key characteristics in intelligent manufacturing systems

Farid

2014

J Intell Manuf

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In recent years, the fields of reconfigurable manufacturing systems, holonic manufacturing systems, and multi-agent systems have made technological advances to support the ready reconfiguration of automated manufacturing systems. While these technological advances have demonstrated robust operation and been qualitatively successful in achieving reconfigurability, limited effort has been devoted to the measurement of reconfigurability in the resultant systems. Hence, it is not clear (1) to which degree these designs have achieved their intended level of reconfigurability, (2) which systems are indeed quantitatively more reconfigurable and (3) how these designs may overcome their design limitations to achieve greater reconfigurability in subsequent design iterations. Recently, a reconfigurability measurement process based upon axiomatic design knowledge base and the design structure matrix has been developed. Together, they provide quantitative measures of reconfiguration potential and ease. This paper now builds upon these works to provide a set of composite reconfigurability measures. Among these are measures for the key characteristics of reconfigurability: integrability, convertibility, and customization, which have driven the qualitative and intuitive design of these technological advances. These measures are then demonstrated on an illustrative example followed by a discussion of how they adhere to requirements for reconfigurability measurement in automated and intelligent manufacturing systems.

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Self-organizing multiagent mechatronic systems in perspective

Cited by 7 publications

References 24 publications

A Computational Framework for Procedural Abduction Done by Smart Cyber-Physical Systems

A Computational Framework for Procedural Abduction Done by Smart Cyber-Physical Systems

Challenges and Properties for Bio-inspiration in Manufacturing

Measures of reconfigurability and its key characteristics in intelligent manufacturing systems

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