MétaCan
Menu
Back to cohort
Record W4224247240 · doi:10.1155/2022/2486561

Transportation of Payload Using Multiple Quadrotors via Rigid Connection

2022· article· en· W4224247240 on OpenAlexaff
Ti Chen, Jinjun Shan, Hugh H. T. Liu

Bibliographic record

VenueInternational Journal of Aerospace Engineering · 2022
Typearticle
Languageen
FieldComputer Science
TopicRobotic Path Planning Algorithms
Canadian institutionsYork UniversityUniversity of Toronto
FundersNational Natural Science Foundation of China
KeywordsPayload (computing)Controller (irrigation)Position (finance)RobotControl theory (sociology)Control engineeringComputer scienceControl (management)Orientation (vector space)Rigid bodyField (mathematics)EngineeringSimulationArtificial intelligenceMathematics

Abstract

fetched live from OpenAlex

Due to the limited payload capability of an aerial robot, multiple quadrotors can be used to manipulate payloads in aerial transportation, construction, and assembly tasks. This paper focuses on the cooperative transportation of a payload rigidly attached to multiple quadrotor bodies. These quadrotors may have different orientations. The dynamics equation of a rigid body in 3-D space is derived to describe the motion of such a transportation system. Robust position and attitude controllers are designed to drive the system to the desired pose. To assign control signals for each quadrotor, the control command allocation method compatible with the case that partial or all quadrotors are in parallel planes is developed. Finally, experimental results are presented to validate the effectiveness of the proposed controllers and control command allocation methods. Different from classical works in this field, this paper can solve the dynamics modeling, controller design, and control command allocation problems for the transportation of a rigidly connected payload using a team of quadrotors with different orientations.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

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 science0.0010.001
Research integrity0.0000.000
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 head0.011
GPT teacher head0.233
Teacher spread0.222 · 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 designBench or experimental
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

Citations11
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of Aerospace EngineeringSame topicRobotic Path Planning AlgorithmsFrench-language works237,207