TERRAIN ROUGHNESS ASSESSMENT FOR HIGH SPEED UGV NAVIGATION IN UNKNOWN HETEROGENEOUS TERRAINS
Bibliographic record
Abstract
This paper addresses the problem of determining the maximum allowable speed (V) of a vehicle traversing unknown off-road terrains. The calculated maximum speed achieves the fastest navigation without exceeding an allowable range of transmitted force (Fall) to the vehicle's frame. The proposed system enables the vehicle to transit between different terrains safely. The system's input are: (i) a 3D range image of the terrain and (ii) the vehicle's dimensions and characteristics (e.g., suspension parameters). First the terrain roughness is assessed; then the corresponding maximum allowable speed is calculated. In this paper a novel Roughness Index (RI) is used to represent the terrain roughness. This index is calculated based on the standard deviation of the terrain points' elevations (3D range image). A closed form expression of the maximum allowable vehicle speed is developed (as function of the vehicle's properties, Fall, RI, and probability of not exceeding Fall). The proposed system can be used as a driver assistant system to enhance the vehicle performance, increase its life time, and reduce the maintenance cost. In addition, it is a key module in Unmanned Ground Vehicles (UGVs) navigation systems; as it provides the navigation system with necessary information for path and speed planning.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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 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".