A Framework to Enable Eco‐Cyber‐Physical Systems for Robotics‐Focused Digital Twins in Smart Farming

ABSTRACT This paper introduces the R‐ECPS (Robotics‐focused Eco‐Cyber‐Physical System) framework for the development of robotics‐enabled digital twins, integrating human knowledge into the decision‐making process. Our framework adapts ECPS and Digital Twin (DT) definitions to agricultural field robotics. We illustrate its application by envisioning an autonomous digital twin for smart livestock management. The presented preliminary implementation includes a digital testbed, an autonomous environmental parameter mapping algorithm, and a foraging model. Development choices are the result of interdisciplinary collaboration across field robotics, biology, and nutrition. Initial results from a proof‐of‐concept trial reveal both challenges and opportunities for improvement. Despite limitations in the use case early implementation, our framework provides a roadmap for future steps and aims at laying the foundation for advancing robotics‐focused agricultural digital twins.

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Publication Details

Journal
Journal of Field Robotics
Published
2025-04-21
DOI
https://doi.org/10.1002/rob.22562
Citations
3
Primary Topic
Digital Transformation in Industry
Type
article
Field-Weighted Citation Impact
3.73

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article

A Framework to Enable Eco‐Cyber‐Physical Systems for Robotics‐Focused Digital Twins in Smart Farming

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3 citations
Journal of Field Robotics
Digital Transformation in Industry
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article

A Framework to Enable Eco‐Cyber‐Physical Systems for Robotics‐Focused Digital Twins in Smart Farming

Salah Sukkarieh, David Raubenheimer, Stephen J. Simpson, Fiorella Sibona, Nathan Wallace, N. M. Harrison, L. A. González, Christopher Stevens
article en
3 citations

Abstract

ABSTRACT This paper introduces the R‐ECPS (Robotics‐focused Eco‐Cyber‐Physical System) framework for the development of robotics‐enabled digital twins, integrating human knowledge into the decision‐making process. Our framework adapts ECPS and Digital Twin (DT) definitions to agricultural field robotics. We illustrate its application by envisioning an autonomous digital twin for smart livestock management. The presented preliminary implementation includes a digital testbed, an autonomous environmental parameter mapping algorithm, and a foraging model. Development choices are the result of interdisciplinary collaboration across field robotics, biology, and nutrition. Initial results from a proof‐of‐concept trial reveal both challenges and opportunities for improvement. Despite limitations in the use case early implementation, our framework provides a roadmap for future steps and aims at laying the foundation for advancing robotics‐focused agricultural digital twins.

Journal of Field RoboticsVol. 42(6)
The University of Sydney (AU), Australian Centre for Robotic Vision (AU)
University of Sydney, Australian Centre for Field Robotics
Openalex Percentile: Top 7%
Digital Transformation in Industry
3.73
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