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Record W4384943037 · doi:10.1139/tcsme-2022-0156

Analysis of kinematic characteristics of 2-P(RPS + UPS) parallel mechanism with six degrees of freedom

2023· article· en· W4384943037 on OpenAlexvenueno aff
Yu Wang, Xuejian Ma, Yundou Xu, Zhiyong Wu, Jiantao Yao, Yongsheng Zhao

Bibliographic record

VenueTransactions of the Canadian Society for Mechanical Engineering · 2023
Typearticle
Languageen
FieldEngineering
TopicRobotic Mechanisms and Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsWorkspaceMechanism (biology)KinematicsDecoupling (probability)Revolute jointControl theory (sociology)Computer sciencePosition (finance)Screw theoryParallel manipulatorDegrees of freedom (physics and chemistry)SingularitySimulationEngineeringMathematicsControl engineeringGeometryPhysicsArtificial intelligenceClassical mechanicsRobot

Abstract

fetched live from OpenAlex

At present, there is an increasing demand for the workspace required for the assembly of components, and a parallel mechanism with a large workspace is urgently needed to make up for it. Therefore, in this paper, a 2-P(RPS + UPS) parallel mechanism (where S, P, R, and U represent spherical, prismatic, revolute, and universal joints) with four branched chains and six degrees of freedom is proposed and its kinematic characteristics are analyzed. First, the speed of the 2-P(RPS + UPS) parallel mechanism is numerically derived and verified by simulation using ADAMS software. Then, the motion decoupling characteristics of the mechanism are analyzed from the expression of the speed and position relationship between the input and output of the mechanism. To minimize the force on each branch in the workspace, scale optimization of the 2-P(RPS + UPS) mechanism and the Stewart mechanism is performed. Further, analysis of the dexterity, singularity, and workspace of the 2-P(RPS + UPS) mechanism is conducted, and the performance of the mechanism is compared with that of the Stewart mechanism. The proposed parallel mechanism has fewer branches, a simple kinematic model, strong motion decoupling, a high bearing capacity, a large workspace, and a low initial position height. Moreover, it can be conveniently transported and stored. Therefore, it has good application prospects in assembly and posture adjustment scenes requiring large workspaces.

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)0.000
metaresearch head score (Gemma)0.001
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.010
GPT teacher head0.190
Teacher spread0.180 · 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
GenreEmpirical

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

Citations1
Published2023
Admission routes1
Has abstractyes

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