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Record W2362050632

Region Segmentation of UAV Path Planning

2012· article· en· W2362050632 on OpenAlex
Xuan Liu

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

VenueJisuanji fangzhen · 2012
Typearticle
Languageen
FieldComputer Science
TopicRobotic Path Planning Algorithms
Canadian institutionsMcGill University
Fundersnot available
KeywordsPath (computing)Motion planningDiagonalComputer sciencePoint (geometry)Block (permutation group theory)Line segmentAny-angle path planningSegmentationLine (geometry)Plane (geometry)Computer visionArtificial intelligenceAlgorithmMathematicsGeometry
DOInot available

Abstract

fetched live from OpenAlex

The path of unmanned aerial vehicles should be calculated according to the building known by the city itself as well as flight capability of itself before executing mission and accomplish the mission by tracking the path.This paper represented a method of path planning of UAV based on Block model of urban buildings at the given starting point and destination point.The proposed algorithm mainly contains two parts,first the algorithm simulated the buildings in urban environment with cylinders,and second,after calculating the curved surface of UAV's flight plane,this algorithm proposed an optimal path planning method of UAV.The path is a polygonal line along the equant diagonal line on the searching area of UAV,getting the smallest shading area of this path and simulated.The experiment results demonstrate that this method can complete planning mission efficiently,obtain a desirable route,and have important practical significance.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.705
Threshold uncertainty score0.441

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 communication0.0000.001
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 head0.050
GPT teacher head0.303
Teacher spread0.252 · 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