Intelligent built-in torque sensor for harmonic drive systems
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
A harmonic drive is a compact, light-weight and high-ratio torque transmission device which is used in many electrically actuated robot manipulators. In this paper a built-in torque sensor for harmonic drive systems is examined in detail. The method proposed by Hashimoto (1989), in which strain-gauges are directly mounted on the flexspline, is employed and improved. To minimize sensing inaccuracy, four Rosette strain gauges are used employing an accurate positioning method. To cancel the torque ripples, the oscillation observed on the measured torque and caused mainly by gear teeth meshing, Kalman filter estimation is used. A simple forth order harmonic oscillator proved to accurately model the torque ripples. Moreover, the error model is extended to incorporate any misalignment torque. By on line implementation of the Kalman filter, it has been shown that this method is a fast and accurate way to filter torque ripples and misalignment torque. Hence, the intelligent built-in torque sensor is a viable and economical way to measure the harmonic drive transmitted torque and to employ that for torque feedback strategies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it