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Record W1549272287 · doi:10.1109/iembs.2003.1280881

Development of an hexapod biomicrorobot with Nafion-Pt IPMC microlegs

2003· article· en· W1549272287 on OpenAlexaff
Martin J.-D. Otis, Roch Bernier, Yann Pasco, H. W. Menard, Hicham Semmaoui, M. Jarry, Réjean Fontaine

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicRobotic Locomotion and Control
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsHexapodPulse-width modulationRobotMicrocontrollerComputer scienceServomotorMaterials scienceEngineeringControl theory (sociology)SimulationMechanical engineeringElectrical engineeringComputer hardwareControl (management)

Abstract

fetched live from OpenAlex

This paper presents an artificial locomotion servo-system for an insect like hexapod biomicrorobot (BMR). This servo-system, programmed with VHDL code, will act as a driver in a RISC architecture microcontroller to reproduce insect tripod walking. An overview of the robot control system, in accordance with the insect displacement principle, is demonstrated with timing parameters. A control algorithm of the six legs driving the robot in any direction versus pulse width modulation (PWM) is reviewed. BMR microlegs are built with cylindrical Nafion-Pt ionomeric polymer-metal composite (IPMC) that have 2.5 degrees of freedom. Specific fabrication process for one leg is exposed. Dynamic behavior and microleg characteristics have been measured in deionized water using a laser vibrometer. BMR current consumption is an important parameter evaluated for each leg. Hardware test bench to acquired measurement is presented. The purpose of this design is to control a BMR for biomedical goals like implantation in human body. Experimental results on the proposed legs are conclusive for this type of bioinspired BMR.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.005

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.006
GPT teacher head0.181
Teacher spread0.175 · 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 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

Citations15
Published2003
Admission routes1
Has abstractyes

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