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Record W3009365510 · doi:10.1115/1.4045724

Workspace-Based Design of Equivalent Compression Spring Legs for a 3-DoF Translational Tensegrity Robot

2019· article· en· W3009365510 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 · 2019
Typearticle
Languageen
FieldEngineering
TopicStructural Analysis and Optimization
Canadian institutionsLaurentian University
Fundersnot available
KeywordsTensegrityWorkspaceRobotDeflection (physics)EngineeringStiffnessSnake-arm robotSimulationStructural engineeringComputer scienceControl theory (sociology)Control engineeringRobot kinematicsMobile robotArtificial intelligencePhysicsClassical mechanics

Abstract

fetched live from OpenAlex

Abstract Prior work has led to the development of a three-degree-of-freedom translational tensegrity robot. Due to inherent and unavoidable collisions between the struts of the tensegrity system that is used as the basis of the robot’s development, these were replaced by equivalent compression spring legs (ECSLs). In this paper, multiple ECSL design concepts are proposed to generate the desired force-deflection behavior, with some based on the use of variable radius drums (VRDs). The optimization of the VRD profiles based on the desired force-deflection behavior is demonstrated. The ECSL designs are then compared based on the corresponding size of the robot’s workspace. The impact on its workspace of the robot’s orientation with respect to the gravitational field is analyzed as is the amount of preload introduced in the robot’s members by the ECSLs. Recommendations on preferred ECSL designs are made for different applications.

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: Methods
Teacher disagreement score0.424
Threshold uncertainty score0.273

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 head0.019
GPT teacher head0.217
Teacher spread0.198 · 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