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Experimental verification of an LiDAR based Gust Rejection System for a Quadrotor UAV

2022· article· en· W4288047701 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.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

Venue2022 International Conference on Unmanned Aircraft Systems (ICUAS) · 2022
Typearticle
Languageen
FieldEngineering
TopicAerospace and Aviation Technology
Canadian institutionsnot available
FundersCranfield UniversityMcMaster University
KeywordsLidarAnemometerRangingEnvironmental scienceRemote sensingWind speedFeed forwardWind directionComputer scienceMeteorologyMarine engineeringEngineeringGeologyPhysics

Abstract

fetched live from OpenAlex

This paper assesses the use of a ground-based wind measuring LiDAR (Light Detection and Ranging) for remote sensing of incoming wind gusts at the landing site of an autonomous quadrotor. The experimental verification results show that the scalar measurements from the LiDAR were able to recreate the horizontal wind vector even with wind direction variation. Comparisons were conducted against conventional cup anemometers with wind vanes, and these show a good correlation. Upwind LiDAR measurements were used to predict the downstream wind using a transport model. This prediction compared with the downwind measurement shows a good correlation. This wind preview information from the LiDAR is then incorporated into a disturbance feedforward control scheme to increase the gust resilience of the vehicle. Simulation and experimental results demonstrate the system's efficacy.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.717
Threshold uncertainty score0.968

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 head0.023
GPT teacher head0.258
Teacher spread0.235 · 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