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Record W2514332889 · doi:10.1109/med.2016.7535886

Real-time autonomous take-off, tracking and landing of UAV on a moving UGV platform

2016· article· en· W2514332889 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
FieldComputer Science
TopicRobotic Path Planning Algorithms
Canadian institutionsConcordia University
Fundersnot available
KeywordsUnmanned ground vehicleTracking (education)Controller (irrigation)Computer scienceLinear-quadratic regulatorControl theory (sociology)Vehicle dynamicsRemotely operated underwater vehicleControl engineeringEngineeringReal-time computingControl (management)Artificial intelligenceAerospace engineeringMobile robotRobot

Abstract

fetched live from OpenAlex

This paper presents a control strategy for take-off, tracking, and landing of a quadrotor unmanned aerial vehicle (UAV) on an unmanned ground vehicle (UGV) to be applied to missions of forest fires monitoring, detection, and fighting and other applications. A combination of sliding mode control (SMC) and linear quadratic regulator (LQR) is presented as the UAV local controller, while pure-pursuit strategy is applied as the UGV controller. Leader-follower formation controller approach is used during take-off, tracking and landing phases based on SMC. Experimental results are presented in order to demonstrate the performance of the team in different scenarios.

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.887
Threshold uncertainty score0.355

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 head0.025
GPT teacher head0.250
Teacher spread0.224 · 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

Citations51
Published2016
Admission routes1
Has abstractyes

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