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Record W3002553957 · doi:10.1115/1.4046049

A C-Legged Monopedal Robot and Its Transition From Multiple Locomotion Modes

2020· article· en· W3002553957 on OpenAlexafffund
Guochao Bai, Bernhard Thomaszewski

Bibliographic record

VenueJournal of Mechanisms and Robotics · 2020
Typearticle
Languageen
FieldEngineering
TopicRobotic Locomotion and Control
Canadian institutionsUniversité de Montréal
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsRobotActuatorSimulationComputer scienceLegged robotRobot locomotionProcess (computing)Controller (irrigation)Mode (computer interface)Frame (networking)Set (abstract data type)Control theory (sociology)Control engineeringEngineeringMobile robotRobot controlArtificial intelligenceControl (management)

Abstract

fetched live from OpenAlex

Abstract This paper introduces a minimalistic design of a monopedal robot (monobot) with C-shaped legs which can achieve multiple locomotion modes (multi-mode) such as walking, leaping, as well as backward and forward flipping. The monobot contains an actuator, speed controller, 3D-printed base frame and legs, and battery set. The weight of the whole robot is less than 80 g. Dimensional parameters are optimized to simplify the design process and to identify effective factors for locomotion. Potential locomotion modes of the robot are analyzed by dynamics simulation. A simplified virtual prototype is tested within the multibody simulation software. An experimental platform of the monobot is also developed. The speed of the platform is adjusted to verify the correspondence between the actuator speed and locomotion mode as obtained by simulation. Potential applications of the multi-mode monobot include disaster rescue, planet exploration, and reconnaissance.

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 score0.001
Threshold uncertainty score0.004

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)0.0010.000

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 head0.015
GPT teacher head0.183
Teacher spread0.169 · 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

Citations5
Published2020
Admission routes2
Has abstractyes

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