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Aerobatics on Three-Dimensional Paths for Agile Fixed-Wing Unmanned Aerial Vehicles

2022· article· en· W4288047709 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

Venue2022 International Conference on Unmanned Aircraft Systems (ICUAS) · 2022
Typearticle
Languageen
FieldEngineering
TopicGuidance and Control Systems
Canadian institutionsMcGill University
Fundersnot available
KeywordsFixed wingAgile software developmentDecoupling (probability)ThrustAerodynamicsNonlinear systemController (irrigation)Control theory (sociology)Computer scienceControl engineeringPath (computing)EngineeringAerospace engineeringWingControl (management)Artificial intelligence

Abstract

fetched live from OpenAlex

This work presents a nonlinear path-following strategy designed to enable an agile fixed-wing UAV to follow three-dimensional geometric paths while simultaneously performing different aerobatic maneuvers about its thrust axis. While agile fixed-wings and similar platforms are designed expressly for aerobatic flight, these maneuvers are seldom used in autonomous operation due to their complexity. The proposed system expands the autonomous capabilities the platform by allowing an independent roll command to be prescribed while still ensuring the UAV follows the intended path. This controller is built around a geometric attitude control system, a roll-decoupling velocity control, and a guidance law that steers the UAV onto the intended path through velocity commands. To verify the properties of the proposed control system, experimental outdoor flight tests are conducted. Results show the versatility of the control system, since arbitrary roll maneuvers can be achieved without additional aerodynamic characterization.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.238
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.026
GPT teacher head0.240
Teacher spread0.214 · 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