Buffering Performance Analysis of an Ostrich-like Leg Based on a Seven-Link Parallel Mechanism
Bibliographic record
Abstract
As one of the fastest running animals on land, the ostrich’s excellent athletic ability benefits from its unique leg structure. Based on the idea of bionics, this paper intends to obtain a kind of robotic leg structure with a similar buffering capacity to that of the ostrich. For this purpose, the structural characteristics of a seven-link parallel mechanism are analyzed firstly, having some specific features similar to ostrich legs, such as the center of mass (COM) located at the root of the leg, a large folding/unfolding ratio, and so on. Then, the kinematic model of the bionic leg is established, and the energy storage of the flexible parts of the leg is investigated. Finally, an impact experiment of the structure onto the ground is carried out to verify the accuracy of the established kinematic model. This paper systematically reveals the nonlinear law of the elasticity of an ostrich-like leg and provides the buffering performance characteristics of the leg in the process of hitting the ground, based on its elastic properties by the kinematic model and the experiment.
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) | 0.002 | 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".