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Record W3043708377 · doi:10.1109/lra.2020.3010208

Improving Multirotor Landing Performance on Inclined Surfaces Using Reverse Thrust

2020· article· en· W3043708377 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

VenueIEEE Robotics and Automation Letters · 2020
Typearticle
Languageen
FieldEngineering
TopicAerospace Engineering and Energy Systems
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsTouchdownThrustLanding gearPayload (computing)Aerospace engineeringMultirotorRotor (electric)Marine engineeringRange (aeronautics)TowingGeologySimulationEngineeringComputer scienceMechanical engineering

Abstract

fetched live from OpenAlex

Conventional multirotors are unable to land on inclined surfaces without specialized suspensions and adhesion devices. With the development of a bidirectional rotor, landing maneuvers could benefit from rapid thrust reversal, which would increase the landing envelope without involving the addition of heavy and complex landing gears or reduction of payload capacity. This letter presents a model designed to accurately simulate quadrotor landings, the behavior of their stiff landing gear, and the limitations of bidirectional rotors. The model was validated using experimental results on both low-friction and high-friction surfaces, and was then used to test multiple landing algorithms over a wide range of touchdown velocities and slope inclinations to explore the benefits of reverse thrust. It is shown that thrust reversal can nearly double the maximum inclination on which a quadrotor can land and can also allow high vertical velocity landings.

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)0.000
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: Empirical
Teacher disagreement score0.114
Threshold uncertainty score0.555

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)0.0000.000

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 head0.019
GPT teacher head0.197
Teacher spread0.178 · 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