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Record W4229515360 · doi:10.1115/detc2008-49518

On the Ability of a Cable-Driven Robot to Generate a Prescribed Set of Wrenches

2008· article· en· W4229515360 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicModular Robots and Swarm Intelligence
Canadian institutionsUniversité Laval
FundersFonds Québécois de la Recherche sur la Nature et les TechnologiesCanada Research Chairs
KeywordsWrenchSet (abstract data type)WorkspaceTask (project management)PolytopeRepresentation (politics)Computer scienceRobotConstruct (python library)Topology (electrical circuits)MathematicsArtificial intelligenceEngineeringCombinatoricsMechanical engineering

Abstract

fetched live from OpenAlex

This paper presents a new geometry-based method to determine if a cable-driven robot operating in a d-degree-of-freedom workspace (2 ≤ d ≤ 6) with n ≥ d cables can generate a given set of wrenches in a given pose, considering acceptable minimum and maximum tensions in the cables. To this end, the fundamental nature of the Available Wrench Set is studied. The latter concept, defined here, is closely related to similar sets introduced in [23, 4]. It is shown that the Available Wrench Set can be represented mathematically by a zonotope, a special class of convex polytopes. Using the properties of zonotopes, two methods to construct the Available Wrench Set are discussed. From the representation of the Available Wrench Set, computationally-efficient and non-iterative tests are presented to verify if this set includes the Task Wrench Set, the set of wrenches needed for a given task.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.636
Threshold uncertainty score0.401

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 head0.054
GPT teacher head0.227
Teacher spread0.173 · 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

Quick stats

Citations18
Published2008
Admission routes2
Has abstractyes

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