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Record W2362050632

Region Segmentation of UAV Path Planning

2012· article· en· W2362050632 on OpenAlexaff
Xuan Liu

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.012
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.000
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 head0.050
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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreMethods

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2012
Admission routes1
Has abstractyes

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