Models for Slip Estimation and Soft Terrain Characterization With Multilegged Wheel–Legs
Bibliographic record
Abstract
Successful operation of off-road mobile robots faces the challenge of mobility hazards posed by soft, deformable terrain, e.g., sand traps. The slip caused by these hazards has a significant impact on tractive efficiency, leading to complete immobilization in extreme circumstances. This paper addresses the interaction between dry frictional soil and the multilegged wheel-leg concept, with the aim of exploiting its enhanced mobility for safe, in situ terrain sensing. The influence of multiple legs and different foot designs on wheel-leg-soil interaction is analyzed by incorporating these aspects to an existing terradynamics model. In addition, new theoretical models are proposed and experimentally validated to relate wheel-leg slip to both motor torque and stick-slip vibrations. These models, which are capable of estimating wheel-leg slip from purely proprioceptive sensors, are then applied in combination with detected wheel-leg sinkage to successfully characterize the load bearing and shear strength properties of different types of deformable soil. The main contribution of this paper enables nongeometric hazard detection based on detected wheel-leg slip and sinkage.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".