A High-Fidelity Simulation Platform for Industrial Manufacturing by Incorporating Robotic Dynamics Into an Industrial Simulation Tool
Bibliographic record
Abstract
Simulation provides an efficient and safe evaluation solution for industrial automation to pretest software before deploying it in real systems. However, only high-fidelity simulation environments that precisely reconstruct the behavioral patterns of real systems can guarantee a successful transfer from simulation to reality (sim-to-real). Many existing industrial simulation tools provide libraries for various industrial devices, which simplify the development efforts significantly, but they generally lack the ability to model the system dynamics and often fail to generate a realistic representation when the system performance is sensitive to the modeling deviation. For example, robots equipped with intelligent algorithms potentially lead to task failure if the software is sensitive to the variation of the system dynamics. In this paper, we design a novel simulation platform for industrial manufacturing use cases consisting of a cooperative robot and a modular manufacturing device. With the dynamic model of the robot integrated into a manufacturing digital-twining software, the platform achieves high simulation fidelity by incorporating the effect of the robot dynamics to the control logic of the industrial tasks. Also, the simulation can exchange data with the real robot via an open protocol, which enables the simultaneous test of the real and simulated systems. Two experiments are conducted on the simulation platform to validate its fidelity in terms of the consistent control logic with the real system. Also, a workpiece distribution use case is studied to show how the simulation platform is used to develop a task-planning algorithm for a manufacturing application.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".