High-Fidelity Dynamic Modeling and Simulation of Planetary Rovers Using Single-Input-Multi-Output Joints With Terrain Property Mapping
Bibliographic record
Abstract
Planetary rovers may traverse terrains with complex geometries and variable physical properties, but their mobility behaviors are complicated and difficult to simulate precisely. This article focuses on high-fidelity dynamic modeling and simulation for a type of rovers that incorporate single-input-multi-output joints to enhance terrain adaptability, which has been used on China's Tianwen-1 Mars rover. A novel multibody dynamic model and its solutions are derived first with consideration of single-input-multi-output joints. Then, a unified terramechanics model is proposed, considering variable terrain surfaces and covering rover's motion states of skidding, slipping, and steering, solved the problem of simulation instability caused by model switching between soft and hard terrains. As the contact areas of wheels with various terrains and resultant sinkage are dominant factors to ensure fidelity but difficult to determine, a new terrain modeling method for calculating contact area and wheel sinkage is developed using digital elevation map with physical properties. A simulation system is developed, integrating all the above models, and verified with physical experiments and commercial software. The relative simulation errors that have been achieved are less than 5.9% for bogie angles, 6.1% for drawbar pull, and 3.4% for slip ratios, demonstrating high fidelity simulation results.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".