The effects of reduced-gravity on planetary rover mobility
Bibliographic record
Abstract
One of the major challenges faced by planetary exploration rovers today is the negotiation of difficult terrain, such as fine granular regolith commonly found on the Moon and Mars. Current testing methods on Earth fail to account for the effect of reduced gravity on the soil itself. This work characterizes the effects of reduced gravity on wheel–soil interactions between an ExoMars rover wheel prototype and a martian soil simulant aboard parabolic flights producing effective martian and lunar gravitational accelerations. These experiments are the first to collect wheel–soil interaction imagery and force/torque sensor data alongside wheel sinkage data. Results from reduced-gravity flights are compared with on-ground experiments with all parameters equal, including wheel load, such that the only difference between the experiments is the effect of gravity on the soil itself. In lunar gravity, a statistically significant average reduction in traction of 20% is observed compared with 1 g, and in martian gravity an average traction reduction of 5–10% is observed. Subsurface soil imaging shows that soil mobilization increases as gravity decreases, suggesting a deterioration in soil strength, which could be the cause of the reduction in traction. Statistically significant increases in wheel sinkage in both martian and lunar gravity provide additional evidence for decreased soil strength. All of these observations (decreased traction, increased soil mobilization, and increased sinkage) hinder a rover’s ability to drive, and should be considered when interpreting results from reduced-load mobility tests conducted on Earth.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 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".