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Concurrent optimal design of modular robotic configuration

2001· article· en· W2072955639 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Robotic Systems · 2001
Typearticle
Languageen
FieldEngineering
TopicModular Robots and Swarm Intelligence
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsModular designComputer scienceMathematical optimizationGenetic algorithmOptimization problemRobotSelf-reconfiguring modular robotDiscrete optimizationMulti-objective optimizationControl engineeringEngineeringMathematicsArtificial intelligenceRobot controlProgramming languageMobile robot

Abstract

fetched live from OpenAlex

This paper presents a new optimization design methodology that is applicable to modular systems. This new methodology is called concurrent optimization design method (CODM). A modular robot is taken as a case study. The CODM is superior to the existing methods for modular robot configuration design in the sense that traditional type synthesis and dimensional synthesis now can be treated once. This mathematically implies that (i) variables are defined for both types and dimensions, and (ii) all the variables are defined in one optimization problem formulation. This paper illustrates that, in fact, optimization design for modular architectures necessitates a multiobjective optimization problem. A genetic algorithm is used to solve for this complex optimization model which contains both discrete and continuous variables. © 2001 John Wiley & Sons, Inc.

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.

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)0.001
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: Empirical · Consensus signal: none
Teacher disagreement score0.984
Threshold uncertainty score0.563

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.037
GPT teacher head0.244
Teacher spread0.207 · 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