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Record W2049119305 · doi:10.1115/1.4027234

Analysis and Optimization of One-Degree of Freedom Robotic Legs

2014· article· en· W2049119305 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

VenueJournal of Mechanisms and Robotics · 2014
Typearticle
Languageen
FieldEngineering
TopicRobotic Locomotion and Control
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsTraverseDegrees of freedom (physics and chemistry)Process (computing)Computer scienceSimulationWork (physics)RobotReliability (semiconductor)TerrainControl theory (sociology)EngineeringArtificial intelligenceMechanical engineering

Abstract

fetched live from OpenAlex

Almost all walking robots are composed of two or more multi-degrees-of-freedom (DOFs) legs which give them a good ability to traverse obstacles. Nevertheless, their speed and efficiency when traversing rough terrains is, in most cases, arguably limited. Additionally, they have the disadvantage of a generally lower reliability. The design of robust and efficient 1-DOF leg is, on the other hand, a complex process. In this paper, a method to analyze and optimize 1-DOF robotic legs is proposed. The results of a virtual simulation are used in combination with some performance indices to optimize the geometric parameters of 1-DOF legs. Finally, the results of the simulation and the actual walking performance of a prototype using four legs with the computed optimal parameters are presented and compared with the simulator results. The validation of the simulation model and the optimization method proposed in this paper represents the main contribution of this work.

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: Methods · Consensus signal: none
Teacher disagreement score0.657
Threshold uncertainty score0.341

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.011
GPT teacher head0.196
Teacher spread0.185 · 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