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Record W1610994738 · doi:10.1109/icinfa.2015.7279378

Sense and collision avoidance of Unmanned Aerial Vehicles using Markov Decision Process and flatness approach

2015· article· en· W1610994738 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
TopicAir Traffic Management and Optimization
Canadian institutionsConcordia University
Fundersnot available
KeywordsCollision avoidanceFlatness (cosmology)Markov decision processTrajectoryComputer scienceCollisionMarkov processProcess (computing)Collision avoidance systemControl theory (sociology)Markov chainScheme (mathematics)Vehicle dynamicsSimulationControl engineeringMathematical optimizationEngineeringArtificial intelligenceMathematicsAerospace engineeringControl (management)Machine learning

Abstract

fetched live from OpenAlex

This paper presents a new development of collision avoidance algorithm that ensures an Unmanned Aerial Vehicle (UAV) can avoid multiple intruders autonomously. Firstly, the Markov Decision Process (MDP) based approach generates the multiple threats resolution logic for the collision avoidance system. Secondly, the optimal trajectory is smoothed by the differential flatness technique where the constraints of the UAV dynamics are considered. In such a way, the planned trajectory is feasible for the UAV. The effectiveness of the developed scheme is illustrated by the numerical simulation studies.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.138
Threshold uncertainty score0.312

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 head0.015
GPT teacher head0.227
Teacher spread0.212 · 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

Citations27
Published2015
Admission routes1
Has abstractyes

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