EFFECTS OF RAMP ANGLE AND MASS DISTRIBUTIONS ON PASSIVE DYNAMIC GAIT — AN EXPERIMENTAL STUDY
Bibliographic record
Abstract
A bipedal walking mechanism with knees is designed and built to study the passive dynamic gait. The effects of changing the ramp angle and the mass distributions of the thighs and the shanks on the gait patterns and walking robustness are studied. It is shown that the changes in the ramp angle and the mass distribution have significant effects on the step lengths and the robustness (the successful rate of launching and the step-count) of the passive gait. More specifically, as the ramp angle increases or the mass center of the entire walker is raised, the step length increases, which dictates the walking speed. However, our experiments show that the changes in the ramp angle and the mass distribution have slight effects on the step period. The optimal ramp angle and mass distribution of the passive walker are also identified, of which the passive walker has the highest successful rate of launching and the step-count. Our experimental results are compared with previous work based on simulations. This research can provide important information for validating/adjusting mathematical models of passive dynamic walking. The work also enables us to gain a better understanding of the mechanics of walking.
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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 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".