Experimental Data for Digital Twin-Based Robotic Cell Scheduling

128,361 bytes of experimental data support a framework for joint optimization in a dynamic robotic cell with robot anomalies. The dataset underpins research on a digital twin-based real-time monitoring and scheduling system. It is intended for validating the proposed framework's performance against robot failures and dynamic events.

Use Cases

• Benchmarking scheduling algorithms under simulated robot anomalies
• Developing digital twin models for real-time robotic cell monitoring
• Testing optimization frameworks for dynamic manufacturing environments
• Validating real-time control strategies against experimental event logs

Strengths

• Dataset is openly licensed under CC-BY-4.0, facilitating reuse and distribution
• Cross-platform presence on figshare indicates established sharing and versioning
• Data is directly tied to a specific, peer-reviewed research framework

Limitations

• Column names and a precise row count are not provided, limiting immediate understanding of data structure
• Author and organizational provenance are anonymized, reducing traceability
• The dataset description is brief and does not detail specific experimental parameters or variables

Provenance

Source

Authors (for anonymous review purpose)

Collection Method

Experimental data generated for a specific research manuscript.

Time Range

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Freshness

Last updated on 2026-03-21

Geography

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