Adaptive robotic manufacturing using higher order knowledge systems

Narendrakrishnan Neythalath; Asbjørn Søndergaard; Jakob Andreas Bærentzen

doi:10.1016/j.autcon.2021.103702

Adaptive robotic manufacturing using higher order knowledge systems

Narendrakrishnan Neythalath^*
, Asbjørn Søndergaard
, Jakob Andreas Bærentzen

^*Corresponding author for this work

ODICO A/S

Research output: Contribution to journal › Journal article › Research › peer-review

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Abstract

Despite a well-understood potential to increase productivity of the global construction industry and sustained, international research efforts in recent years, wide-scale adoption of robotic technology currently remains elusive in the industry. As part of a larger industrial research effort to increase the efficiency of automation technologies within construction, this paper proposes a novel multi-layered knowledge encapsulation model to enable low-cost development of highly diverse robotic control applications within a parametric manufacturing paradigm. The effectiveness of proposed theoretical framework has been validated by developing multiple industrial applications and resulted in almost 40% reduction in development time.

Original language	English
Article number	103702
Journal	Automation in Construction
Volume	127
Number of pages	7
ISSN	0926-5805
DOIs	https://doi.org/10.1016/j.autcon.2021.103702
Publication status	Published - Jul 2021

Bibliographical note

Funding Information:
This work is partly funded by the Innovation Fund Denmark (IFD) under File No. 7038-00108A .

Publisher Copyright:
© 2021 Elsevier B.V.

Keywords

Automation
Construction 4.0
Process optimization
Robotics

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title = "Adaptive robotic manufacturing using higher order knowledge systems",

abstract = "Despite a well-understood potential to increase productivity of the global construction industry and sustained, international research efforts in recent years, wide-scale adoption of robotic technology currently remains elusive in the industry. As part of a larger industrial research effort to increase the efficiency of automation technologies within construction, this paper proposes a novel multi-layered knowledge encapsulation model to enable low-cost development of highly diverse robotic control applications within a parametric manufacturing paradigm. The effectiveness of proposed theoretical framework has been validated by developing multiple industrial applications and resulted in almost 40\% reduction in development time.",

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note = "Funding Information: This work is partly funded by the Innovation Fund Denmark (IFD) under File No. 7038-00108A . Publisher Copyright: {\textcopyright} 2021 Elsevier B.V.",

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AU - Neythalath, Narendrakrishnan

AU - Søndergaard, Asbjørn

AU - Bærentzen, Jakob Andreas

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Y1 - 2021/7

N2 - Despite a well-understood potential to increase productivity of the global construction industry and sustained, international research efforts in recent years, wide-scale adoption of robotic technology currently remains elusive in the industry. As part of a larger industrial research effort to increase the efficiency of automation technologies within construction, this paper proposes a novel multi-layered knowledge encapsulation model to enable low-cost development of highly diverse robotic control applications within a parametric manufacturing paradigm. The effectiveness of proposed theoretical framework has been validated by developing multiple industrial applications and resulted in almost 40% reduction in development time.

AB - Despite a well-understood potential to increase productivity of the global construction industry and sustained, international research efforts in recent years, wide-scale adoption of robotic technology currently remains elusive in the industry. As part of a larger industrial research effort to increase the efficiency of automation technologies within construction, this paper proposes a novel multi-layered knowledge encapsulation model to enable low-cost development of highly diverse robotic control applications within a parametric manufacturing paradigm. The effectiveness of proposed theoretical framework has been validated by developing multiple industrial applications and resulted in almost 40% reduction in development time.

KW - Automation

KW - Construction 4.0

KW - Process optimization

KW - Robotics

U2 - 10.1016/j.autcon.2021.103702

DO - 10.1016/j.autcon.2021.103702

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VL - 127

JO - Automation in Construction

JF - Automation in Construction

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Adaptive robotic manufacturing using higher order knowledge systems

Abstract

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Keywords

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