A Nonlinear Volumetric Contact Model for Planetary Rover Wheel/Soil Interaction
Bibliographic record
Abstract
A novel nonlinear planetary rover wheel/soil interaction model based on the properties of the penetration volume is proposed. This approach allows for fast multibody dynamic simulation of planetary rover operations by providing a closed-form solution for the contact forces. For the derivation of this contact model, the normal stress distribution under the planetary rover wheel is assumed to be a nonlinear function of the soil compression. The soil foundation with which the wheels of a robotic vehicle may interfere is represented by a continuum of hyperelastic springs. A linear version of this representation of the contact interface as a mattress of springs has been derived previously [1] and validated for the simulation of the interaction between two relatively inflexible objects [2]. However, for contacts involving large deformations, the nonlinear material properties have to be considered. In this paper, a brief summary of the elastic foundation model is given and the extension of the model to a hyperelastic foundation is provided so that it can be used for the intended purpose of modelling rover wheels in contact with soft terrain. A solution for the integral of the normal stress distribution of the rover wheels is proposed and verified in a number of numerical experiments by comparing the results against Bekker’s analytical approach. Although the focus of this work is on wheel/soil interaction, the general form of the volumetric contact modelling approach can be applied to any hyperelastic contact problem.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".