Control of a Haptic Interface and a Micromachining Setup With an Open Source Real-Time System
Bibliographic record
Abstract
Open source software have evolved into powerful tools for real-time control of mechatronic systems. They are of interest for academic purposes, as they promise high flexibility at low cost. In this paper, we investigate the performance of an open source control architecture based on the Linux Real-Time Application Interface (RTAI) through two applications, a haptic interface for surgery simulation and a setup for the micromachining of glass. Both applications demand a compact real-time system with sufficient precision (i.e. low jitter) for online process control and data acquisition. The real-time control has been realized on a laptop with a USB-port data acquisition card. Performance measurement results show that high quality real-time control can be performed up to 2kHz via USB communication, which is sufficient for a large range of robotic or mechatronic applications. Drawbacks like the need for considerable knowledge of Linux internals for successful installation as well as the current limitations are discussed and strategies are proposed to overcome these. Both setups are currently being used for ongoing research as well as for educational purposes.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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) | 0.007 | 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".