Vehicle-terrain interaction models for analysis and performance evaluation of wheeled rovers
Bibliographic record
Abstract
In this work, a multibody dynamics model of a wheeled mobile robot is developed to characterize the terrain reaction forces in terms of the physical and control parameters of the system. A common strategy for simulating the motion of mobile robots on soft soil is to compute the soil reaction forces using terramechanics models and to solve a forward dynamics problem by considering the soil reactions as a set of forces applied to the system. This intends to provide an accurate computation of the forces involved in the wheel-soil interaction; however, a series of factors such as the sensitivity of reaction forces to soil parameters limits the applicability of the existing terramechanics models in unstructured environments. We propose an alternative approach which does not rely on the soil properties, but at the same time does not intend to provide an exact computation of wheel-soil interaction forces. The main objective of this approach is to estimate the effect of changes in control and design parameters on the performance of the system, using the information provided by the dynamics model of the vehicle. To this end, the reaction forces for the wheel-terrain interaction in the ideal limit case of pure rolling and no penetration are obtained upon the specification of the motion at the contact points, via kinematic constraints. The validity of the analysis results obtained using the proposed paradigm is verified by simulation runs and experiments. The experimental results suggest that this approach is successful in predicting the variation of a set of important performance indicators in terms of the changes in the parameters of the system.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".