Bibliographic record
Abstract
This paper addresses the minimization of the risk of wheel slippage for a popular class of rovers. In the absence of any constraints on the system (e.g., force/torque balance and maximum motor torques), the optimal traction solution is known to be that with equal “friction requirements” (ratios of tractive to normal force) for all wheels. Nevertheless, the current state of the art is to routinely perform computationally expensive constrained optimization because of the presumed importance of the constraints in a real system. The contribution of this paper is a thorough investigation of the configuration space for four-wheel rovers, driving straight over rough terrain, in search of configurations where the unconstrained optimal answer does or does not satisfy the constraints, and, thus, is or is not valid. Equal “friction requirements” are added to the four-wheel rover's system of quasi-static equations and a valid solution is sought to this augmented system of equations. It is found that the equal “friction requirements” solution is almost always valid, except for the case where two of the wheels are wedged against opposing vertical faces, a highly unusual and unlikely scenario. Therefore, we can conclude that computationally expensive constrained optimization is not required to achieve traction control for four-wheel rovers.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".