Analysis on Wheel–Ground Contact Load Characteristics of Unmanned Off-road Vehicles
Bibliographic record
Abstract
The wheel-ground contact load characteristics of unmanned ground vehicles are an important foundation for vehicle design, structural parameter optimization, off-road performance evaluation, and control strategy formulation.The load characteristics of unmanned ground vehicles are mainly investigated based on traditional vehicle terramechanics theory, which cannot reflect wheel-ground contact.This study proposed a model integrated with qualitative theoretical analysis and quasi-quantitative simulation to evaluate wheel-ground contact load characteristics during the off-road movement of unmanned vehicles.Prediction and test models of system wheel contact load characteristics were built by multi-physical field coupling analysis.Flow and power characteristics during unilateral steering were discussed systematically through terramechanics theory.The accuracy of the models was verified by experiments.Results show that changes in the tire load affect the average stress on the ground contact surface of tire, which leads to the forward gravity center of the entire machine.The optimal combination of structural parameters under dynamic working conditions of the unmanned vehicles is determined based on multi-physics coupling analysis model to optimize the structural design.The load pressure of the system reaches 19.53 MPa in the accelerated start-up phase, and the error of simulation and test results is within 10%.This study provides tools for theoretical and simulation analysis for development of the optimized structure design and control strategy formulation of unmanned ground vehicles.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".