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Record W2095386133 · doi:10.1115/1.3147184

A New Method to Calculate the Force and Moment Workspaces of Actuation Redundant Spatial Parallel Manipulators

2009· article· en· W2095386133 on OpenAlexaff
Venus Garg, Juan A. Carretero, Scott Nokleby

Bibliographic record

VenueJournal of Mechanisms and Robotics · 2009
Typearticle
Languageen
FieldEngineering
TopicRobotic Mechanisms and Dynamics
Canadian institutionsOntario Tech UniversityUniversity of New Brunswick
Fundersnot available
KeywordsWorkspaceMoment (physics)WrenchCartesian coordinate systemComputer scienceActuatorMotion planningTorqueControl theory (sociology)RobotMathematicsEngineeringMechanical engineeringGeometryArtificial intelligenceClassical mechanicsPhysicsControl (management)

Abstract

fetched live from OpenAlex

A new method for obtaining the force and moment workspaces of spatial parallel manipulators (PMs) is presented. Force and moment workspaces are regions within which a manipulator can sustain/apply at least a certain value of force or moment in all directions. Here, the force and moment workspaces are found using a method, which explicitly sets the largest possible number of actuators to their maximum limits ensuring that the manipulator is performing at its best possible wrench capabilities. Two cases for obtaining these workspaces are used. The first gives the applicable/sustainable force with a prescribed moment whereas the second one gives the applicable/sustainable moment with a prescribed force. For illustration purposes, the method is applied to a six-degree-of-freedom (DOF) redundantly-actuated spatial PM, the 3-RRṞS. The results are represented graphically as the boundaries of the workspace in the three-dimensional Cartesian space. These workspaces can be used as a powerful tool for path/task planning and PM design.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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: Methods · Consensus signal: Methods
Teacher disagreement score0.005
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.009
GPT teacher head0.244
Teacher spread0.235 · 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 source (direct Gemma or distilled Codex), 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

Citations11
Published2009
Admission routes1
Has abstractyes

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