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Record W2899321373 · doi:10.1115/detc2018-85174

Three Formulations of Sling Load Dynamics for UAV Motion Planning and Control

2018· article· en· W2899321373 on OpenAlexaff
Alexander Cicchino, Inna Sharf

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicRobotic Path Planning Algorithms
Canadian institutionsMcGill University
Fundersnot available
KeywordsPayload (computing)Control theory (sociology)Equations of motionElasticity (physics)Computer scienceClassical mechanicsPhysicsControl (management)

Abstract

fetched live from OpenAlex

In this paper, the sling load dynamics of an aerial vehicle carrying a payload are investigated by employing three formulations of the governing equations. They are the hybrid formulation where the system exists in either a taut cable or slack cable configuration, with appropriate treatment of the transition between the two; the linear complementarity problem (LCP) formulation where the cable constraints are imposed as linear complementarity conditions and finally, the lumped parameter formulation where the cable is modelled with a series of spring-mass elements. The hybrid and LCP formulations neglect the elasticity of the cable while the lumped parameter model explicitly accounts for the elastic properties of the cable, albeit in a discrete way. The importance of the incorporation of elastic properties of the cable on the system is investigated for the variation in solution space of the payload. The three formulations are compared numerically, for information on the computational cost, motion of the payload, and tension profile, for several aerial maneuvers, including an aggressive obstacle avoidance with a window clearance flight.

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: Methods · Consensus signal: none
Teacher disagreement score0.937
Threshold uncertainty score0.251

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 head0.027
GPT teacher head0.280
Teacher spread0.253 · 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
GenreMethods

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
Published2018
Admission routes1
Has abstractyes

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