Hardware-in-loop simulation of robots interacting with environment via algebraic differential equation
Bibliographic record
Abstract
Hardware-in-the-loop simulation is an attractive tool to validate the functionality of space robots. It is the purpose of the hardware-in-the-loop simulation to make a rigid robot prototype behave, in contact/noncontact phases, as closely as possible to a given robot reference model that would be encountered in the real-world. We introduce a method for the hardware-in-the-loop simulation that is based on the projection of the generalized coordinates of the robot model into two subspaces representing independent and dependent coordinates. The independent coordinate is obtained via simulation by making use of its acceleration model expressed in a closed form. While, the dependent coordinate is obtained from the generalized coordinated of the linearized robot prototype acting as a double integrator on the input dependent-coordinate acceleration. It is followed by investigating the effect of external disturbance on the performance of the hardware-in-the-loop simulation, by the method of Lagrangian multiplier. Finally experimental results obtained from implementation of a single axis arm is presented.
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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 | 0.001 |
| 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 | 0.000 |
| 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".