Experimentally Evaluating Granular Scaling Laws for Predicting Lunar-Gravity Wheel Performance in Cohesive Regolith
Bibliographic record
Abstract
Traversing granular regolith, especially in reduced gravity environments, remains a potential challenge for wheeled rovers. Mitigating hazards for planetary exploration rovers requires testing in representative environments, but direct Earth-based testing fails to account for the effect of reduced gravity on the soil itself. Granular scaling laws (GSL) have been proposed in the literature to predict performance of a larger wheel based on tests with a smaller wheel, or to predict performance in one gravity level based on tests in another gravity level. Recent experimental results by the authors suggest that GSL offers an accurate and conservative method for predicting wheel performance in reduced gravity based on 1-g experiments, at least in cohesionless soil. For cohesive soils, an additional constraint has been proposed in the literature stating that the radius ratio must be the inverse of the gravity ratio. For example, to predict wheel performance in lunar-g based on 1-g tests, the wheel radius in 1-g must be 1/6 of the wheel radius in lunar-g (since the gravity on Earth is 6× higher than lunar gravity). To date, this has only been validated using numerical simulations. This work considers this proposed GSL cohesion constraint for lunar regolith, which is estimated to be mildly cohesive (0.1–1 kPa). Testing a 1/6-scaled lunar rover may not be practical or desirable, so the error introduced by ignoring this cohesion constraint should be quantified. To evaluate this cohesion constraint, preliminary experiments have been performed measuring performance of single wheels of different radii driving through the lunar soil simulant LMS-1, which has cohesion similar to the estimated cohesion of lunar regolith. Results obtained using these wheels will later be compared to experiments with a larger wheel on parabolic flights producing effective lunar gravity. The two wheels tested in 1-g have radii equal to 1/6 and 1/2 of the radius of the wheel that will be flown in lunar-g. Applying GSL to preliminary results from these two wheels, dimensionless drawbar pull (i.e., net traction), sinkage, and power draw were calculated. Since tests with the 1/6-radius wheel followed the cohesion constraint and tests with the 1/2-radius wheel did not, the error introduced by ignoring this constraint can be estimated based on the difference between the dimensionless results from tests with each wheel. Results were similar (i.e., not statistically significantly different) for the two wheels, which suggests that ignoring the cohesion constraint may not have a significant effect for the low levels of cohesion present in lunar regolith. However, drawbar pull was negative for both wheels at all slips tested, indicating that the thrust generated by the wheel was lower than the resistance. Future experiments will be performed in different conditions that generate positive drawbar pull to make a more conclusive comparison, in addition to the planned lunar-g experiments.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".