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An Energy Analysis of Quadrotors with Cable-Suspended Payloads

2022· article· en· W4288047704 on OpenAlexaff
Hassan Alkomy, Jinjun Shan

Bibliographic record

Venue2022 International Conference on Unmanned Aircraft Systems (ICUAS) · 2022
Typearticle
Languageen
FieldEngineering
TopicRobot Manipulation and Learning
Canadian institutionsYork University
Fundersnot available
KeywordsEnergy (signal processing)Computer scienceAerospace engineeringMarine engineeringElectrical engineeringEngineeringPhysics

Abstract

fetched live from OpenAlex

This paper aims to perform a qualitative energy analysis of quadrotors with cable-suspended payloads. First, the dynamics model of this system is presented, then the concept of energy quotient is introduced to help qualitatively assess quadrotor’s energy consumption without prior knowledge of quadrotor-specific parameters such as propeller and motor specifications. After that, a detailed mathematically-oriented energy analysis is preformed to investigate the effect of payload mass, cable length, quadrotor’s polynomial trajectory degree and average acceleration on quadrotor’s energy consumption. The analysis is enforced by simulations and validated experimentally. The results show that increasing payload mass, polynomial trajectory’s average acceleration and/or polynomial degree increases quadrotor’s energy consumption monotonically. It also shows that there exists an optimized cable length, at which, quadrotor’s energy consumption is minimum.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.331
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.000

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 head0.023
GPT teacher head0.248
Teacher spread0.225 · 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

Citations2
Published2022
Admission routes1
Has abstractyes

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