Contact Stiffness and Damping Estimation for Robotic 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
In this paper, we review and compare four algorithms for the identification of contact stiffness and damping during robot constrained motion. The intended application is dynamics modeling and simulation of robotic assembly operations in space. Accurate simulation of these tasks requires contact dynamics models, which in turn use contact stiffness and damping to calculate contact forces. Hence, our primary interest in identifying contact parameters stems from their use as inputs to simulation software with contact dynamics capability. Estimates of environmental stiffness and damping are also valuable for force tracking and stability of impedance controllers. The algorithms considered in this work include: a signal processing method, an indirect adaptive controller with modifications to identify environment damping, a model reference adaptive controller and a recursive least-squares estimation technique. The last three methods have been proposed for real-time implementation in impedance and force-tracking controllers. The signal processing scheme uses a frequency estimate calculated with fast Fourier transform of the force signal and is an off-line method. The algorithms are first evaluated using numerical simulation of a benchmark test. Experiments conducted with a robotic arm contacting a flexible wall provide a further demonstration of their performance. Our results indicate that the indirect adaptive controller has the best combination of performance and ease of use. In addition, the effect of persistently exciting signals is discussed.
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 | 0.002 | 0.001 |
| 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