Identification of Friction Coefficient for Constrained Robotic Tasks
Bibliographic record
Abstract
Correct modeling of friction forces during constrained robotic operations is critical to high-fidelity contact dynamics simulation. Such simulations are particularly important for the development, mission planning and operations analysis of space robotic systems. Most existing friction models employ the coefficient of friction to capture the relationship between the friction force and the normal load. Hence, accurate identification of this parameter is prerequisite to accurate simulation. This issue is particularly important for space robotic operations since friction characteristics of materials are very different in space. In this manuscript, the problem of identification of the coefficient of friction is investigated experimentally and numerically. The motivating application being space manipulator systems, our principal objective is to develop a practical off-line identification algorithm, requiring minimum number of measurements from sensors available on space robots. To this end, a strategy is proposed to determine the coefficient of friction by using only the measured end-effector forces. The key idea behind the method is that during one-point contact, these forces represent the contact force and hence, can be directly used to calculate the coefficient of friction. The proposed approach is tested with the experimental data from peg insertion experiments conducted on a planar robotics test-bed with a specially designed contact interface. The algorithm is generalized to arbitrary complex geometries and applied to identify the coefficient of friction for a simulated battery drop test.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".