MétaCan
Menu
Back to cohort
Record W4294982655 · doi:10.1109/lra.2022.3205122

Experimental Performance Comparison of Bidirectional Actuator Configurations for Suspended Aerial Platforms

2022· article· en· W4294982655 on OpenAlexaff
Julien Rachiele-Tremblay, Hughes La Vigne, Guillaume Charron, David Rancourt, Alexis Lussier Desbiens

Bibliographic record

VenueIEEE Robotics and Automation Letters · 2022
Typearticle
Languageen
FieldComputer Science
TopicRobotic Path Planning Algorithms
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsThrustPayload (computing)ActuatorBandwidth (computing)Marine engineeringComputer sciencePropellerSampling (signal processing)SimulationAerospace engineeringEngineeringTelecommunicationsArtificial intelligence

Abstract

fetched live from OpenAlex

Suspended payload requires bidirectional thrust actuation to control their motion. Such systems, like the small suspended aerial cliff sampling system considered in this letter, require strong bidirectional actuators capable of fine positioning and fast disturbance rejection to fight wind gusts. This letter presents a detailed experimental comparison of three bidirectional thrust actuator configurations (i.e., reverse thrust, antagonist thrusters, and variable pitch propeller) to understand their characteristics at the scale of small aerial systems. Five tests highlight the strengths and weaknesses of each configuration regarding static thrust capabilities, large and small step response, and bandwidth capabilities. This information is then used to select the best configuration for the suspended aerial cliff sampling system described, which in turn was able to sample rare cliff plants in Kaua'i in winds gusts of up to 37 km/h. The results will also help aerial roboticists make better-informed design decisions.

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)0.001
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: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0030.000

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 head0.032
GPT teacher head0.284
Teacher spread0.252 · 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
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueIEEE Robotics and Automation LettersSame topicRobotic Path Planning AlgorithmsFrench-language works237,207