A Multibody Dynamics Framework for Simulation of Rovers on Soft Terrain
Bibliographic record
Abstract
A new framework is developed for efficient implementation of semi-empirical terramechanics models in multibody dynamics environments. In this approach, for every wheel in contact with soft soil, unilateral contact constraints are added for both the normal direction and the tangent plane. The forces associated with the latter, like traction and rolling resistance, are formulated in this approach as set-valued force laws, their properties being determined by deregularization of the terramechanics relations. As shown in the paper, this leads to the dynamics representation in the form of a linear complementarity problem (LCP). With this formulation, stable simulation of rovers is achieved even at relatively large time steps. In addition, a high-resolution height-field (HF) is employed to model terrain-surface deformation and changes in hardening of soil under the wheel. As a result, the multipass effect is captured in the presented approach. In addition, an extensive set of experiments was conducted using a version of the Juno rover (Juno II). The experimental results are analyzed and compared with the model developed in the paper.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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) | 0.003 | 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".