Authors:
Jeshwitha Jesus Raja
;
Meenakshi Manjunath
and
Marian Daun
Affiliation:
Center of Robotics, Technical University of Applied Sciences Würzburg-Schweinfurt, Schweinfurt, Germany
Keyword(s):
Requirements Engineering, Goal Modeling, Digital Twin, Industry Automation, Robotic Systems.
Abstract:
In many smart manufacturing scenarios of Industry 4.0, robots play a vital role. Robotic systems allow for au-tomatization and semi-automatization of individual work tasks using standard hardware. Thus, production and assembly processes can be flexibly redefined during operation. In addition, human workers can be supported for complex and specific work tasks where full automation by industrial production systems is not possible or not cost-efficient. To monitor current process execution, to predict process outcome, and to ensure safe behavior of the robots at runtime, digital twins are seen as a vital part of future smart manufacturing. How-ever, current industrial approaches typically define the digital twin on the go, i.e. when the factory has been build and equipped with robotic systems. Thus, the absence of systematic planning of the digital twin leads to unused potential for more complex analysis, monitoring, and prediction tasks of digital twins commonly suggested in research.
This is partly due to the absence of structured software and systems engineering approaches for the development of robotic systems. In this paper, we explore the use of goal modeling to systematically define the robotic system, its monitoring system, and the digital twin. Application to case examples shows that this lightweight approach aligns with industry preferences to focus on technical challenges rather than invest too much effort in a thorough yet cost intensive engineering approach, while at the same time allowing for the proper definition of robots and their digital twins.
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