Advantages of slanted grousers for skid-steer planetary rovers with rigid wheels
Bibliographic record
Abstract
Lunar exploration activities around the world are driving development of low mass skid-steer rovers, for which rigid wheels with grousers are common. Wheels with slanted grousers (i.e. that span from the inner to the outer edge of the wheel surface at an angle, rather than directly across) are compared favorably in this work against V-offset shaped grousers in skid-steer point turn performance, without any reduction in slope climbing performance. Single wheel tests are conducted in GRC-1 lunar simulant with the wheels oriented along a representative slip angle corresponding to skid-steer point turning. Slanted grousers achieve positive tangent turning force, F T , a metric introduced to identify conditions when a wheel can sustain skid-steer point turning. The slanted grouser achieves a positive F T at slip ratios below 0.4 compared to as much as 0.6 for V-offset, while also only experiencing approximately half as much sinkage. On the other hand, there is little to no difference in performance in straight line driving, relevant for nominal driving and slope climbing. Full rover tests with 4 appropriately configured slanted grouser wheels validate point turn and slope climbing performance with an average skid-steer point turn slip ratio of approximately 0.35 and 0.8 for slope climbing. • Slanted grousers are superior for point turns with skid-steer rovers. • No disadvantage is found for slanted grousers in straight-line slope climbing. • Tangent turning force metric is introduced for evaluating skid-steer point turns.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".