Digital Twin for Pre-Deployment Validation of AI-Driven Safety-Critical Industrial Edge Control Loops
To fill this gap, this paper proposes a modular DT framework for industrial environments that jointly models physical processes, wireless communications, and application logic within a unified architecture.
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Key points
- Industrial environments are increasingly characterized by the tight interaction among physical processes, communication infrastructures, and intelligent applications.
- In this context, Digital Twins (DTs) have emerged as a key technology for system analysis and optimization.
- The feasibility of the proposed framework is experimentally validated through a real-world Proof-of-Concept (PoC) implemented in the BI-REX pilot line, involving a 5G-connected Autonomous Mobile Robot (AMR) transporting hazardous liquids and remotely controlled by an AI-driven application.
- Moreover, the analysis shows how inaccuracies of network modeling can critically affect the feasibility of latency-sensitive industrial control loops, highlighting the potential of integrated DTs as tools for the pre-deployment design and validation of next-generation industrial systems.
Sources (1)
- [1]Digital Twin for Pre-Deployment Validation of AI-Driven Safety-Critical Industrial Edge Control LoopsarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 8, 01:28 PM
To fill this gap, this paper proposes a modular DT framework for industrial environments that jointly models physical processes, wireless communications, and application logic within a unified architecture.
Industrial environments are increasingly characterized by the tight interaction among physical processes, communication infrastructures, and intelligent applications.
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