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Record W4205966649 · doi:10.2514/6.2022-0757

Model of UAV and Downwash for Multi-UAV Path Planning

2022· article· en· W4205966649 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 2022 Forum · 2022
Typearticle
Languageen
FieldComputer Science
TopicRobotic Path Planning Algorithms
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsDownwashMotion planningComputer sciencePath (computing)Aerospace engineeringArtificial intelligenceComputer networkEngineeringRobot

Abstract

fetched live from OpenAlex

View Video Presentation: https://doi.org/10.2514/6.2022-0757.vid This paper develops a method to model the air flow downwash effect generated by the quadrotor unmanned aerial vehicle (UAV) and its effect on the neighboring UAVs. The downwash model derives the resultant downwash force and torque and takes the UAV attitude into the account. Each UAV is shaped by a virtual structure for collision-free path planning. The shape is modified from a standard spherical body to a proposed cylinder to better minimize downwash impact. A flock-based path planning algorithm and an optimal reciprocal collision avoidance (ORCA) algorithm are implemented and investigated in this study to analyze the downwash effect and the performance of the proposed cylindrical shape UAV model. The downwash model simulation shows how the UAV can be affected when it counters the downwash air flow. A flock-based algorithm and an ORCA algorithm along with the spherical and cylindrical shape UAV models are simulated to demonstrate the cylindrical model can improve path planning performance.

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: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.272
Threshold uncertainty score0.761

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.060
GPT teacher head0.292
Teacher spread0.232 · 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