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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 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 · 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.

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 score0.175
Threshold uncertainty score0.522

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 head0.012
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