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The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

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A Modular Digital Twinning Framework for Safety Assurance of Collaborative Robotics.

J A Douthwaite¹, B Lesage², M Gleirscher³

¹Department of Automatic Control and Systems Engineering, University of Sheffield, Sheffield, United Kingdom.

Frontiers in Robotics and AI

|January 3, 2022

Summary

This study introduces a modular digital twinning framework for assessing safety in collaborative robotic manufacturing. The framework enables virtual safety validation, enhancing robotic safety assurance in industrial settings.

Keywords:

automated code generation collaborative robot safety digital twins human-robot collaboration modular framework probabilistic model checking risk-informed software synthesis robotics

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Area of Science:

Robotics
Cyber-Physical Systems
Manufacturing Engineering

Background:

Digital twins are emerging as powerful tools for managing complex robotic processes.
Ensuring safety in collaborative robotic manufacturing presents significant challenges.
Existing frameworks may lack modularity and scalability for diverse applications.

Purpose of the Study:

To present a novel, modular digital twinning framework for investigating safety in collaborative robotic manufacturing.
To facilitate the creation of mixed environments (Digital Models, Shadows, Twins).
To standardize communication and physical system representation across hardware platforms.

Main Methods:

Developed a modular digital twinning framework with scalable representations.
Integrated tools for safety analysis and control within the framework.
Applied the framework to an industrial case study for safety assurance of a collaborative robotic process.
Utilized a supporting validation module and testing strategy for virtual safety assessment.

Main Results:

Demonstrated the framework's application to an industrial collaborative robotic manufacturing case study.
Created a digital twin scenario with individual digital twins for manufacturing entities.
Successfully applied a synthesized safety controller and virtually assessed its safety claims.
Generated adequate evidence for safety validation through the framework.

Conclusions:

The developed digital twinning framework is a versatile research tool for robotic safety assurance.
The framework enables virtual assessment of safety claims in collaborative robotic manufacturing.
Digital twins offer exciting possibilities for enhancing safety in robotic systems.