Contact parameter estimation using a space manipulator verification facility
Bibliographic record
Abstract
Computer simulations play an important role in the design and verification of space robotic operations since on-orbit tests are impossible to conduct before launch. Thus, accurate computer modelling and simulation of space robotic tasks is essential. Of particular difficulty are space manipulator operations, which involve constrained or contact tasks. Here, the contact dynamics capability in the modelling tools becomes critical for high fidelity simulation. This in turn implies a need for accurate determination of contact parameters, which are used as inputs to contact dynamics simulation. In this work, the identification of contact dynamics parameters based on sensor data obtained during robotic contact tasks is considered. The contact parameter estimation problem is addressed for simple and complex contacting geometries using the SPDM Task Verification Facility Manipulator Test-bed (SMT) at the Canadian Space Agency. The SMT is a space-representative robotic simulation facility. Single- and multiple-point contact parameter estimation software toolboxes were developed and used with SMT experiments. Single point SMT contact experiments were performed with six different payloads. The single point toolbox was used as part of the process of identifying payload stiffness from SMT experimental data. Multiple point contact parameter estimation experiments with the SMT were conducted using a mock-up of an International Space Station Arm Computer Unit (ACU) as payload. The multiple point toolbox was used to generate contact stiffness, damping and friction estimates. An evaluation of the sensitivity of the parameter estimation algorithm to mismatches in ACU physical dimensions and ACU geometry files was conducted.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".