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Experiments on Collaborative Transport of Cable-suspended Payload with Quadrotor UAVs

2022· article· en· W4288047686 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venue2022 International Conference on Unmanned Aircraft Systems (ICUAS) · 2022
Typearticle
Languageen
FieldComputer Science
TopicRobotic Path Planning Algorithms
Canadian institutionsMcGill University
FundersNCR
KeywordsPayload (computing)DroneEthernetComputer scienceControl (management)Aerospace engineeringEngineeringAeronauticsComputer network

Abstract

fetched live from OpenAlex

The use of drones to transport cargo is an important application of unmanned aerial vehicles. Given the limited payload capacity of a typical small drone, the notion of utilizing multiple drones to transport heavy payloads presents a promising alternative. This article describes an easy to deploy system of multiple drones with a cable-suspended payload to enable flight testing of guidance, navigation, and control strategies for such systems in realistic operating conditions, outside of a laboratory. A unique aspect of our system is the use of Ethernet cables to ensure fast and reliable communications between vehicles. Deploying the system with a basic leader- follower guidance strategy and the PX4 flight stack for low-level control of each vehicle, we demonstrate collaborative payload transport through an extensive experimental campaign. We are able to autonomously transport payloads up to 2kg with two vehicles and up to 3kg with three off-the-shelf, 1kg vehicles. The paper also presents a brief discussion of failure cases and points to worthwhile directions for further research on this topic.

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.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.708
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0020.000
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 head0.033
GPT teacher head0.276
Teacher spread0.243 · 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