Power and energy consumption of skid‐steer rovers turning on loose soil
Bibliographic record
Abstract
Abstract This study highlights two key phenomena affecting power and energy consumption of skid‐steer rovers on loose soil that is not present on the hard ground: soil excavation due to wheel counterrotation and impeded turning when dragging a braked wheel. Experiments in the field and in a controlled laboratory sandbox show that, on sand, power peaks by 15%–20% in a newly identified range of turns with radii between half the rover width, , to , the radius at which the inner wheel does not turn. In this range of turns, the inner wheels rotate backwards but are being dragged forward through piles of sand they excavate by counterrotation. At , turns are shown to take much longer, leading to higher total energy consumption over time. Experiments in a controlled laboratory sandbox isolate the high motor torque and the resistance force experienced when a skid‐steer rover drags a counterrotating or braked wheel, respectively, through loose soil. Other field experiments also demonstrate that paths combining circular arcs and lines can lead to energy savings of up to 15% relative to common ones consisting of point turns and lines; the experimental results suggest the circular arcs should have radii of approximately . The quantitative values presented in this paper are specific to the rover and soils tested, but there are reasons to support the overall conclusions generalizing to all skid‐steer rovers in loose soil.
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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 | 0.000 |
| 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) | 0.002 | 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".