Optimal Traction Forces for Four-Wheel Rovers on Rough Terrain
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it