Bibliographic record
Abstract
View Video Presentation: https://doi.org/10.2514/6.2023-0510.vid The ability to detect and characterize the onset of wheel slippage is crucial for rover operations. This paper presents a new method for defining wheel slippage by considering the size of a virtual wheel that produces the amount of forward motion obtained. From this definition, we consider the implications of this virtual wheel on proprioceptive measurements. Then, by using cross-correlation, we can identify the onset of wheel slippage. Using these observations, we construct a simple 3-layer Feed-Forward Neural Network to identify wheel slippage using the classical definition of slippage and our new virtual wheel size. We compare four networks trained on experimental data obtained from our rover test sandbox and compare the results of the networks on unseen data sets. The networks we trained are able to estimate slippage values to approximately ±0.1 using the classical definition of slip and ±0.4���� of a change in wheel radius using our new definition of slippage (one-sigma bound).
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.003 | 0.002 |
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".