Experimental investigation of lunar prototype wheel traction performance on deformable terrain
Bibliographic record
Abstract
Travelling long distances with maximum reliability are necessary requirements for future lunar rover missions. Rovers' mobility performance highly depends on wheel type and the mechanical properties of the terrain on which it is rolling. On the lunar surface, the terrain is primarily composed of very fine grained abrasive particles called regolith. Traditional pneumatic rubber wheels are not a viable option for planetary rovers, due to the unknown properties of rubber over a long term exposure to radiation, and the chances of failure in the near vacuum environment. Therefore, non-pneumatic non-rubber compliant wheels have been recognized as a best possible option for planetary exploration rovers.The present research thesis focuses on testing procedures and data analysis of different prototype wheels, rolling on dry sand, by considering several wheel-soil performance parameters including traction, slope climbing ability, rolling resistance, and power consumption. Results from these experiments demonstrate some of the main wheel properties that can affect wheel performance at low speed conditions and provide preliminary data for validation of wheel-terrain model simulation performed within the McGill University research group. The tested wheels were designed and tested at McGill University, Montreal, Canada, as part of a partnership program between the Canadian Space Agency, Neptec Design Group and a number of associated organizations.
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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 | 0.001 | 0.000 |
| 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".