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Record W4392542940 · doi:10.32920/25365292

Aerial Grasping of an Object in the Strong Wind: Robust Control of an Aerial Manipulator

2024· preprint· en· W4392542940 on OpenAlexaff
Guangyuan Zhang, Yuqing He, Bo Dai, Feng Gu, Liying Yang, Jianda Han, Guang Jun Liu

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldComputer Science
TopicRobotic Path Planning Algorithms
Canadian institutionsToronto Metropolitan University
FundersScience and Technology Planning Project of Guangdong ProvinceNational Key Research and Development Program of China
KeywordsManipulator (device)Object (grammar)Computer scienceControl (management)Control theory (sociology)Artificial intelligenceControl engineeringComputer visionEngineeringRobot

Abstract

fetched live from OpenAlex

<p>An aerial manipulator is a new kind of flying robot system composed of a rotorcraft unmanned aerial vehicle (UAV) and a multi-link robotic arm. It gives the flying robot the capacity to complete manipulation tasks. Steady flight is essential for an aerial manipulator to complete manipulation tasks. This paper focuses on the steady flight control performance of the aerial manipulator. A separate control strategy is used in the aerial manipulator system, in which the UAV and the manipulator are controlled separately. In order to complete tasks in environments with strong wind disturbance, an acceleration feedback enhanced robust <em>H</em>∞ controller was designed for the UAV in the aerial manipulator. The controller is based on the hierarchical inner-outer loop control structure of the UAV and composed of a robust <em>H</em>∞ controller and acceleration feedback enhanced term, which is used to compensate for the wind disturbance. Experimental results of aerial grasping of a target object show that the controller can suppress the wind disturbance effectively, and make the aerial manipulator hover steadily with sufficient accuracy to complete aerial manipulation tasks in strong wind.</p>

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.485
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0030.001
Research integrity0.0000.001
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 head0.041
GPT teacher head0.285
Teacher spread0.244 · 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.

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

Citations0
Published2024
Admission routes1
Has abstractyes

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