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Record W2137589977 · doi:10.1109/aim.2003.1225545

Design development of a prototype multi-module manipulator

2004· article· en· W2137589977 on OpenAlexafffund
C.W. de Silva, Kenneth Wong, V. J. Modi

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicSpace Satellite Systems and Control
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsRevolute jointActuatorMobile manipulatorSizingDevelopment (topology)Process (computing)Parallel manipulatorControl engineeringEngineeringComputer scienceManipulator (device)SoftwareJoint (building)Control systemTopology (electrical circuits)RobotRobotic armMechanical engineeringStructural engineeringElectrical engineeringArtificial intelligenceMobile robot

Abstract

fetched live from OpenAlex

A deployable manipulator is a robotic system that has a combination of revolute and prismatic joints. Based on this concept, the Multi-module Deployable Manipulator System (MDMS) has been developed. It consists of several modules, each having both revolute and prismatic degrees of freedom, connected in series. This class of manipulators offers several useful characteristics with respect to the dynamics and control. This paper presents the design and development of the Multi-module Deployable Manipulator System (MDMS) at the University of British Columbia. The planar manipulator that is developed here is somewhat unique in that it comprises four modules, each of which has one revolute joint and one prismatic joint, connected in a chain topology. The design process involves the selection and sizing of actuators, the design of mounting and connecting components, and the selection of both electrical devices and software for real-time control. Ground-based manipulator systems are useful in assessing, through real-time experimentation, the effectiveness of control procedures for their possible application to space-based systems.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.789
Threshold uncertainty score0.276

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 head0.028
GPT teacher head0.221
Teacher spread0.193 · 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 designBench or experimental
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

Citations2
Published2004
Admission routes2
Has abstractyes

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