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Record W2734745502 · doi:10.1115/1.4037287

Slip Analysis for a Wheeled Mobile Manipulator

2017· article· en· W2734745502 on OpenAlexaff
Tao Song, Fengfeng Xi, Shuai Guo, Xiaowei Tu, Xianhua Li

Bibliographic record

VenueJournal of Dynamic Systems Measurement and Control · 2017
Typearticle
Languageen
FieldEngineering
TopicControl and Dynamics of Mobile Robots
Canadian institutionsToronto Metropolitan University
FundersBeijing Municipal Science and Technology CommissionScience and Technology Commission of Shanghai Municipality
KeywordsSlip (aerodynamics)InertiaControl theory (sociology)KinematicsMobile manipulatorEngineeringComputer scienceStructural engineeringSimulationMobile robotRobotArtificial intelligenceAerospace engineeringControl (management)Physics

Abstract

fetched live from OpenAlex

A method is presented for slip analysis of a wheeled mobile manipulator. The said system consists of an industrial manipulator mounted on a mobile platform performing aircraft manufacturing tasks. Unlike tracked/legged mobile robots that may slip when negotiating slopes or climbing stairs, a wheeled mobile manipulator may slip resulting from the manipulator movement or the forces from the end-effector during fastening. Slip analysis is crucial to ensure pose accuracy for operation. In this study, first a universal friction constraint is used to derive the slip condition of the system. Three cases are considered, with the first case considering the reaction force in relation to the stand-off distance between the mobile manipulator and the workpiece. The second case deals with the joint speeds to investigate the effect of coupling terms including centrifugal forces and gyroscopic moments on slip. The third case deals with the joint accelerations to investigate the effect of inertia forces and moments on slip. Simulations and experiments are carried out to verify the proposed method.

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 score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.015
GPT teacher head0.224
Teacher spread0.209 · 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

Citations20
Published2017
Admission routes1
Has abstractyes

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