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Record W2077150548 · doi:10.1115/detc2014-34419

Dynamically Feasible Trajectories for Three-DOF Planar Cable-Suspended Parallel Robots

2014· article· en· W2077150548 on OpenAlexafffund
Xiaoling Jiang, Clément Gosselin

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicRobotic Mechanisms and Dynamics
Canadian institutionsUniversité Laval
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsWorkspaceTrajectoryRobotKinematicsControl theory (sociology)Computer scienceParallel manipulatorTranslation (biology)PlanarParametric statisticsPendulumEngineeringMathematicsPhysicsArtificial intelligenceMechanical engineeringClassical mechanics

Abstract

fetched live from OpenAlex

This paper proposes a trajectory planning technique for planar three-degree-of-freedom cable-suspended parallel robots. Based on the kinematic and dynamic modelling of the architecture, force constraints that can guarantee that cables remain under tension are obtained. Periodic parametric trajectories that extend beyond the static workspace are designed. The trajectories involve combined translations and rotations. Substituting the trajectories into the force constraints, interval arithmetics is used to search for global conditions on the trajectory parameters which ensure that the trajectories are feasible. Special frequencies related to combined rotational and translational motions are exposed which can be used to better exploit cable-suspended robots. Moreover, it is observed that the special frequencies related to the translation are akin to the natural frequency of pendulum-like systems. The proposed trajectory planning approach can be used to plan combined rotational and translational dynamic trajectories that can extend beyond the static workspace of the mechanism, thereby opening novel applications and possibilities for cable-suspended robots.

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 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 score0.229
Threshold uncertainty score0.783

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 head0.013
GPT teacher head0.208
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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreMethods

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".

Quick stats

Citations22
Published2014
Admission routes2
Has abstractyes

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