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Record W4285163324 · doi:10.1109/lra.2022.3176102

Adaptative Friction Shock Absorbers and Reverse Thrust for Fast Multirotor Landing on Inclined Surfaces

2022· article· en· W4285163324 on OpenAlexaff
John Bass, Isaac Tunney, Alexis Lussier Desbiens

Bibliographic record

VenueIEEE Robotics and Automation Letters · 2022
Typearticle
Languageen
FieldEngineering
TopicAerospace Engineering and Energy Systems
Canadian institutionsInstitut interdisciplinaire d'innovation technologique
Fundersnot available
KeywordsThrustMultirotorShock (circulatory)Aerospace engineeringDroneLanding gearMarine engineeringEnvelope (radar)Kinetic energyEnvironmental scienceGeologyEngineeringPhysics

Abstract

fetched live from OpenAlex

Small multirotors are not capable of landing in complex situations, such as on inclined surfaces, in wind gusts or at high impact velocities. This paper explores the use of lightweight friction shock absorbers, combined with rapid thrust reversal, to increase the landing envelope of a quadrotor. The friction shock absorbers serve to dissipate the drone’s kinetic energy and the reverse thrust increases the maximum slope inclination at which it can land. A landing gear prototype was designed and implemented on a DJI F450, and a model was created to generate landing maps to evaluate its benefits. Finally, the technology was tested in real outdoor conditions. The overall system enables drones to safely land on surfaces of up to 60° and at vertical speeds of up to 2.75 m/s, thus increasing the landing envelope by a factor of 8, compared to traditional multirotors.

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: Empirical
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 head0.012
GPT teacher head0.206
Teacher spread0.194 · 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

Citations14
Published2022
Admission routes1
Has abstractyes

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