ROS-Based Control of an Industrial Micro-Assembly Robot
Bibliographic record
Abstract
Today’s manufacturing is progressively challenged by high product variant turnovers, low standardization, and small lot sizes. These characteristics can also be seen in the growing sector of manufacturing of optical systems, in which the micro-assembly of the components is currently the main cost driver. In the sector of industrial robotics, research has addressed these challenges by developing rapidly reconfigurable robotic cells. Typically, these solutions are based on high-level task programming and a hardware and software-agnostic virtualised machine control interface, which is often facilitated by the open-source Robot Operating System (ROS) platform. While research in the domain of micro-assembly has also introduced virtual programming to assembly systems, the focus has rather been on assisting experienced engineers with the implementation of processes than on enabling rapid assembly of prototypes by inexperienced personnel. To bridge this gap, we are working on a holistic framework for autonomous process implementation that is specifically focused on the unique boundary conditions of micro-assembly. As one of the initial steps, in this paper, we present a case study of the implementation of ROS2-based control of an industrial micro-assembly robot. Furthermore, we detail on the advantages, prospects, and limitations, our design choices encompass.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".