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Record W4317583813 · doi:10.2514/6.2023-1345

Data-Driven and Robust Path-following Control of a Quadrotor Slung Load Transport System

2023· article· en· W4317583813 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

VenueAIAA SCITECH 2023 Forum · 2023
Typearticle
Languageen
FieldComputer Science
TopicTarget Tracking and Data Fusion in Sensor Networks
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPayload (computing)Control theory (sociology)Kalman filterTrajectoryComputer sciencePath (computing)Gaussian processGaussianProcess (computing)Extended Kalman filterSystem dynamicsControl engineeringEngineeringControl (management)Artificial intelligencePhysics

Abstract

fetched live from OpenAlex

View Video Presentation: https://doi.org/10.2514/6.2023-1345.vid In this paper, a robust path following control law for a quadrotor Slung Load Transport System is developed. A Gaussian Process-augmented Extended Kalman Filter is proposed to estimate payload states. In this approach, Gaussian Processes are used to compensate for unmodelled dynamics in the process model, and they are trained on previously collected data of a Slung Load Transport System in flight. Both simulations and experiments verify the estimation and control system framework and demonstrate successful stabilization and trajectory tracking of the Slung Load Transport System, overcoming model inaccuracy and disturbances.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.893
Threshold uncertainty score0.815

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0020.001
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 head0.025
GPT teacher head0.246
Teacher spread0.221 · 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