Effect of gravity in wheel/terrain interaction models
Bibliographic record
Abstract
Abstract Predicting the motion of wheeled robots in unstructured environments is an important and challenging problem. The study of planetary exploration rovers on soft terrain introduces the additional need to consider the effect of nonterrestrial gravitational fields on the forces and torques developed at the wheel/terrain interface. Simply reducing the wheel load under Earth's gravity overestimates the traveled distance and predicts better performance than is actually observed in reduced‐gravity measurements. In this paper, we study the effect of gravity on wheel/terrain interaction. Experiments were conducted to assess the effect of reduced gravity on the velocity profile of the soil under the wheel, as well as on the traction force and sinkage developed by the wheel. It was shown that in the velocity field of the soil, the decay of the tangential velocity component becomes gradual with reducing gravity, and the decay of the normal to rim velocity is slower in Lunar gravity. It was also found that wheel flexibility can have an important effect on the dynamics as the contact patch and effective radius vary periodically. These results were then used together with traditional semiempirical terramechanics models to determine and validate the simulated drawbar pull values. The developed simulation model includes the effect of wheel flexibility, dynamic sinkage, and gravity.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".