Characterizing the Effects of Reduced Gravity on Rover Wheel-Soil Interactions using Computer Vision Techniques
Bibliographic record
Abstract
Mitigating potential hazards for planetary rovers posed by soft soils requires testing in representative environments such as with Martian soil simulants in reduced gravity. This work describes the experimentation, methods, and results of a rover-soil visualization technique that produced rich datasets of reduced gravity wheel-terrain interaction. The activities are linked to the upcoming ExoMars space mission, through the use of ExoMars wheel prototype and Martian soil simulant in simulated Martian gravity produced in parabolic flights. The results indicate that, with wheel normal load held equal between experiments, the amount of soil mobilized by wheel-soil interaction increases as gravity decreases. Moreover, the amount of soil mobilized is more sensitive to slip in lower gravity. The results of the visualization analysis suggest a deterioration in the soil resistance and weaker soil bonding at lower gravities, which undermines the rover mobility by reducing the net traction. The results have important implications regarding the practice of using a reduced-mass rover on Earth to assess the performance of a full-mass rover in similar soil on an extraterrestrial surface.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".