Optimum Design and Trafficability Analysis for an Articulated Wheel-Legged Forestry Chassis
Bibliographic record
Abstract
Abstract High trafficability and stability are the most two significant features of the forestry chassis. In this study, in order to improve surface trafficability, a novel articulated wheel-legged forestry chassis (AWLFC) is presented. To balance the trafficability and stability, a serial suspension system which is a combination with the active four-bar linkage articulated suspension (AFLAS) and passive V shape rocker-bogie is proposed. Then, parameter optimization with a comprehensive object function is implemented not only to enhance the trafficability and stability benefit of the structure but also to reduce the wheel slip. After that, through the flexible kinematic model based on screw theory, characteristics such as leveling ability and surface profile accessibility of the chassis are analyzed. The minimum accessible radius is obtained as 3088 mm, and the longitudinal and lateral leveling angle reaches to 22 deg and 28.7 deg separately. The new chassis performs better on leveling ability and surface profile accessibility than the forestry chassis in the current literature. Finally, through the results of simulation and prototype experiment, error rates related to the flexible analysis are reduced by 12.2% and 8.6% compared with the rigid model. Previously inaccessible forestry working environments can be available with the development of AWLFC.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".