Tire force estimation of unmanned ground vehicles on off-road terrains for navigation decisions
Bibliographic record
Abstract
This paper proposes a method of tire force estimation designed for use in navigation decision making for Unmanned Ground Vehicles (UGVs) operating on off-road terrains. This method (called the Centroid Method) uses a 3D point cloud representation of the terrain to determine tire-ground interaction, and a linear spring to determine the corresponding tire forces. Simulation results of tire force estimation of the Centroid Method are compared against the experimentally validated MF-Swift model, including on a fractal terrain surface which simulates a rough off-road terrain. The results show that the Centroid Method performs well for longitudinal and vertical tire force estimation especially for lower frequencies up to about 8-21Hz. Although the Centroid Method does not provide the same detail as the advanced tire models such as MF-Swift, it is significantly less complex and simpler to implement for enabling efficient on-board UGV navigation decision making.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".