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Record W2085107783 · doi:10.1109/iros.2012.6385679

Energy analysis of worm locomotion on flexible surface

2012· article· en· W2085107783 on OpenAlexaff
David Zarrouk, Inna Sharf, Moshe Shoham

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPhysics and Astronomy
TopicMicro and Nano Robotics
Canadian institutionsMcGill University
Fundersnot available
KeywordsCrawlingRobotActuatorWork (physics)Energy (signal processing)Robot locomotionPower (physics)Control theory (sociology)Mechanical energySimulationComputer scienceEfficient energy useFunction (biology)Control engineeringEngineeringMechanical engineeringMobile robotControl (management)MathematicsPhysicsRobot controlArtificial intelligenceElectrical engineering

Abstract

fetched live from OpenAlex

Recent attempts at designing untethered devices for locomotion inside compliant biological vessels, highlighted the requirements for energy efficiency for prolonged duration inside living bodies. Quite a number of studies considered the design and construction of crawling robots but very few focused on the interaction between the robots and the flexible environment. In previous studies, we derived the efficiency, defined as the actual advance divided by the optimal advance, of worm locomotion. In this paper, we analyze the force, minimum energy and power requirements for worm locomotion over flexible surfaces. More importantly, we determine the optimum conditions of locomotion as a function of the number of cells, friction coefficients, stroke length, energy recovery factor, and tangential compliance. Optimality is defined with respect to energy and power requirements. The analytical results are obtained by integrating the force over the actuator motion and alternatively by summing up the overall energy losses due to friction and elastic losses with the surface and the efficient work performed by the robot. The theoretical predictions are compared to numerical simulations modeling worm robots crawling over flexible surfaces and are found in perfect match.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.904
Threshold uncertainty score0.999

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)0.0020.000

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 head0.014
GPT teacher head0.248
Teacher spread0.234 · 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.

Study designBench or experimental
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

Citations2
Published2012
Admission routes1
Has abstractyes

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