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Record W2117551464 · doi:10.1109/icccn.2007.4318004

A virtual decomposition control based communication network for modular robots applications

2007· article· en· W2117551464 on OpenAlexaff
Tom Lamarche, Wen-Hong Zhu

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicModular Robots and Swarm Intelligence
Canadian institutionsCanadian Space Agency
Fundersnot available
KeywordsModular designSelf-reconfiguring modular robotRobotMechatronicsComputer scienceKey (lock)Frame (networking)DecompositionControl engineeringDistributed computingEmbedded systemRobot controlEngineeringMobile robotArtificial intelligenceComputer network

Abstract

fetched live from OpenAlex

Modular (re-configurable) robots have been studied and developed over two decades. Compared to common industrial robot manipulators, modular robots usually end up showing relatively poorer control performance. This is mainly because most of modular robots' designs typically concentrate on mechatronic interfaces, packaging and re-configurable features, leaving little space for dynamics and control considerations. In our current research frame, we are looking at "virtual decomposition control" (VDC) to significantly improve modular robots dynamics and control performance. A key element to successful implementation of the related algorithms has to do with the inter-modules communication link capabilities. Presented here is the first 2 DOF prototype built to demonstrate the feasibility of implementing VDC on a real system, including the Spacewire Bus based communication protocol used.

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.010

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 studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.010
GPT teacher head0.255
Teacher spread0.245 · 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

Citations9
Published2007
Admission routes1
Has abstractyes

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