Adaptive observer for the calibration of the force-moment sensor of a space robot
Bibliographic record
Abstract
A procedure for the calibration of the force-moment sensor of a space robot is reported. In terrestrial applications, such sensors can be calibrated by measuring the sensor output while under known static loads, which are most easily applied using known weights. In zero-g environment, such an approach does not work. A viable alternative is to use the dynamic effects of the motion of a carried payload to load the sensor. For a rigid robot, the displacement of the payload can be inferred from joint-angle measurements using the robot kinematics. However, major space robots are structurally flexible, in which case establishing a similar inference is difficult. Therefore, it is assumed here that a payload with known mass properties is handled by the end-effector, and that the payload displacement is measured directly using a laser vision system. Then, considering the end-effector and the payload to be one rigid-body, their dynamics equations, which are the Newton-Euler equations, are used to design an adaptive observer that simultaneously generates estimates of the motion of the body and identities the calibration matrix of the sensor. In this paper, as a first step, the problem would be solved for planar robots. The performance of the adaptive observer would then be evaluated through simulations
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".