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Record W1899404607 · doi:10.1109/procce.1988.82247

Resource management for a multi-arm robotic assembly cell

2003· article· en· W1899404607 on OpenAlexaffabout
Emil M. Petriu, J.S. Basran

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicScheduling and Optimization Algorithms
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsComputer scienceModularity (biology)Flexibility (engineering)ExploitArchitectureDistributed computingTask (project management)Computer architectureResource management (computing)Object (grammar)RobotSoftware engineeringArtificial intelligenceSystems engineeringEngineering

Abstract

fetched live from OpenAlex

The authors describe the resource management strategy for a multi-arm robotic assembly cell under development at the Electrical Engineering Department of the University of Ottawa. Conceived to support an object-oriented programming language, this assembly cell has a flexible architecture integrating several functional blocks: a task-scheduler, one or more object presentation units, two or more assembly processors, one or more assembly fixtures, and a free-space manager. The architecture and communication aspects are based upon the natural properties of a distributed real-time robotic system. The advantages of such a system are modularity, flexibility, and the ability to exploit the parallelism inherent in complex robot assembly operations. The authors introduce the idea of considering the physical space within which the system operates as a resource that can be managed as an independent entity within the distributed architecture of the robotic system.>

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)0.000
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 score0.007
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.021
GPT teacher head0.237
Teacher spread0.215 · 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
Published2003
Admission routes2
Has abstractyes

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