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Record W4224869330 · doi:10.3390/machines10050306

Buffering Performance Analysis of an Ostrich-like Leg Based on a Seven-Link Parallel Mechanism

2022· article· en· W4224869330 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueMachines · 2022
Typearticle
Languageen
FieldEngineering
TopicRobotic Locomotion and Control
Canadian institutionsDalhousie University
Fundersnot available
KeywordsKinematicsBionicsMechanism (biology)Link (geometry)Computer scienceProcess (computing)Nonlinear systemFolding (DSP implementation)Elasticity (physics)Structural engineeringMathematicsMechanical engineeringEngineeringPhysicsArtificial intelligenceClassical mechanics

Abstract

fetched live from OpenAlex

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.082
Threshold uncertainty score0.524

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.007
GPT teacher head0.202
Teacher spread0.195 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it