Estimating Rover Slope Climbing Ability from Single Wheel Experiments
Bibliographic record
Abstract
Single wheel tests are commonly done in the early design phase of small planetary exploration rovers to predict the rover’s overall mobility performance. This research explores the correlation between single wheel and full rover mobility experimentally. A key contribution is novel single wheel experimentation on inclined terrain. These experiments provide a conceptual bridge between single wheel tests on flat terrain and full rover tests on sloped terrain. Single wheel experimental data from sloped and flat terrain is used to estimate the slip ratio required for the rover to climb terrain inclined at a specific angle. The rover is predicted to climb a slope of 15 degrees with approximately 0.45 wheel slip ratio based on flat terrain data or with 0.6 wheel slip based on sloped terrain data. In fact, full rover experiments exhibit 0.8 slip, demonstrating that single wheel experiments underestimate wheel slip by approximately 0.35 (i.e. almost half of 0.8) in flat ground tests and by 0.2 (i.e. a quarter of the actual value) on sloped terrain. Due to such disparities, predictions of full rover slope climbing ability based on single wheel tests should include additional factors of safety.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".