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Record W2909537343 · doi:10.1109/iros.2018.8593878

A Universal Controller for Unmanned Aerial Vehicles

2018· article· en· W2909537343 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

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdaptive Control of Nonlinear Systems
Canadian institutionsMcGill University
Fundersnot available
KeywordsFixed wingFlight envelopeAgile software developmentController (irrigation)Aerospace engineeringDroneWingEngineeringComputer scienceControl engineeringControl theory (sociology)AerodynamicsControl (management)Artificial intelligence

Abstract

fetched live from OpenAlex

Unmanned aerial vehicles (UAVs) have become popular in a wide range of applications, including many military and civilian uses. State of the art control strategies for these vehicles are typically limited to a portion of the vehicle's flight envelope, and are tailored to a specific type of platform. This article presents a single physics-based controller capable of aggressive maneuvering for the majority of UAVs. The controller is applicable to UAVs with the ability to apply a force along a body-fixed direction, and a moment about an arbitrary axis, which includes UAVs such as multi-copters, conventional fixed-wing, agile fixed-wing, flying-wing with two thrusters, most tailsitters, and some tilt-rotor/wing platforms. We demonstrate autonomous flight for a quadrotor and agile fixed-wing aircraft in a simulation environment. To specifically demonstrate the extreme maneuvering capability of the control logic, we perform a rolling flip with the quadrotor and an aggressive turnaround with the fixed-wing aircraft, all using a single controller with a single set of gains.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.826
Threshold uncertainty score0.371

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 head0.013
GPT teacher head0.222
Teacher spread0.209 · 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

Quick stats

Citations9
Published2018
Admission routes1
Has abstractyes

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