Accurate and Real-time Simulation of Rover Wheel Traction
Bibliographic record
Abstract
Maintaining traction remains one of the challenges that space exploration rovers encounter while roving on Martian or Lunar deformable terrains. Such terrains consist of granular regolith under reduced gravity conditions. Real-time simulation of wheel-soil interactions, that accurately takes gravity effects into account, can improve rovers' online mobility control. The research problem investigated in this paper is the development of a machine learning-based wheel-on-soil simulation model. Machine learning enables efficient and fast mapping of simulation inputs to outputs, trained using high-fidelity (non-real-time) models validated by experiments. The training data is produced by a continuum method comprising a modern constitutive model, nonlocal granular fluidity (NGF), and a state-of-the-art numerical solver, material point method (MPM). Machine learning techniques, including graph network-based simulator (GNS) and principal component analysis (PCA), are proposed to learn efficient mappings. The important aspects that must be captured include the traction forces on the wheel and the behavior of the underlying granular flows. Hence, the experimental data includes force measured using an instrumented single-wheel testbed and subsurface soil motion observed with a high-speed camera and analyzed with optical flow.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".