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Record W2122900185 · doi:10.1115/detc2002/mech-34358

Optimal Configuration Selection for a Cooperating System of Mobile Manipulators

2002· article· en· W2122900185 on OpenAlexafffund
Michel Abou-Samah, Venkat Krovi

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicRobotic Mechanisms and Dynamics
Canadian institutionsMcGill University
FundersMcGill University
KeywordsRevolute jointMobile manipulatorMobile robotKinematicsWorkspaceComputer scienceLinkage (software)Configuration designControl theory (sociology)Selection (genetic algorithm)Robot end effectorEngineeringControl engineeringRobotArtificial intelligence

Abstract

fetched live from OpenAlex

In this paper, we examine the design and selection of optimal configuration of a system of multiple, wheeled mobile manipulators that can team up to cooperatively transport a large common object. Each individually autonomous mobile manipulator consists of a differentially driven wheeled mobile robot (WMR) with a passive, two degree-of-freedom (d.o.f), planar revolute-jointed arm mounted in the plane parallel to the base. The composite multi-degree-of-freedom vehicle, formed by placing a common object on the end-effector of two (or more) such mobile manipulator systems, possesses the ability to change its relative configuration as well as accommodate relative positioning errors of the mobile bases. However, the combination of the non-holonomic constraints of the wheeled mobile bases, the presence of passive joints and closed kinematic loops of the articulations require a careful treatment. The suitable selection of the dimensions of the intermediate linkage and the overall configuration is critical to enhancing the overall performance. In this paper we will focus our attention on the formulation of a design-optimization problem to aid the selection of the configuration of the composite vehicle to attain near-isotropic manipulability of the common object.

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: none
Teacher disagreement score0.841
Threshold uncertainty score0.211

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 head0.012
GPT teacher head0.197
Teacher spread0.185 · 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

Citations15
Published2002
Admission routes2
Has abstractyes

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