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Record W4415432532 · doi:10.1186/s40648-025-00312-7

Detection and tracking quadrotor using surf and feedback linearization sliding mode control

2025· article· en· W4415432532 on OpenAlexaff
Walid Alqaisi, Mostafa Soliman, Ahmed Badawi, Claude Ziad El‐Bayeh

Bibliographic record

VenueROBOMECH Journal · 2025
Typearticle
Languageen
FieldEngineering
TopicAdaptive Control of Nonlinear Systems
Canadian institutionsConcordia University
Fundersnot available
KeywordsControl theory (sociology)Feedback linearizationSliding mode controlLinearizationNonlinear systemTracking (education)Mode (computer interface)Mechatronics

Abstract

fetched live from OpenAlex

This paper presents a quadrotor detection and tracking system that uses a single camera and the Speeded Up Robust Features (SURF) algorithm to extract the image structure and estimate the target's position. The goal is to develop an independent system capable of detecting a followed object’s motion without prior knowledge of its trajectory, without requiring communication with the target, and without relying on external sensors. Feedback Linearization (FL) combined with sliding mode control is used to ensure target tracking. The control system avoids the complex nonlinear control solutions and the highly coupled dynamic behavior of the quadrotor. Nonlinear uncertain disturbances are overcome by using Time Delay Estimation (TDE) of the disturbance.

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 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 score0.698
Threshold uncertainty score0.590

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 head0.013
GPT teacher head0.248
Teacher spread0.235 · 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 teacher head, not a consensus.

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

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

Citations1
Published2025
Admission routes1
Has abstractyes

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