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Record W4221163756 · doi:10.48550/arxiv.2203.02643

SwarmUS: An open hardware and software on-board platform for swarm robotics development

2022· preprint· en· W4221163756 on OpenAlexaff
Étienne Villemure, Philippe Arsenault, Gabriel Lessard, Thierry Constantin, Hubert Dubé, L.-D. Gaulin, Xavier Groleau, Samuel Laperrière, Charles Quesnel, François Ferland

Bibliographic record

VenuearXiv (Cornell University) · 2022
Typepreprint
Languageen
FieldEngineering
TopicModular Robots and Swarm Intelligence
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsSwarm roboticsSwarm behaviourRoboticsEmulationImplementationComputer scienceArtificial intelligenceAnt roboticsRobotEmbedded systemStandardizationSoftwareMobile robotDistributed computingSoftware engineeringRobot controlOperating system

Abstract

fetched live from OpenAlex

Real life implementations of distributed swarm robotics are rare. The standardization of a general purpose swarm robotics platform could greatly accelerate swarm robotics towards real life implementations. The SwarmUS platform is an open-source hardware and software on-board embedded system designed to be added onto existing robots while providing them with swarm features, thus proposing a new take on the platform standardization problem. These features include a distributed relative localization system based on Ultra-Wideband, a local communication system based on Wi-Fi and a distributed coordination system based on the Buzz programming language between robots connected within a SwarmUS platform. Additionally, a human-swarm interaction mobile application and an emulation of the platform in the Robot Operating System (ROS) is presented. Finally, an implementation of the system was realized and tested on two types of robots : a TurtleBot3 Burger and two Pioneer 2DX.

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)0.001
metaresearch head score (Gemma)0.002
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: Methods · Consensus signal: Methods
Teacher disagreement score0.013
Threshold uncertainty score0.043

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0130.006

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 head0.127
GPT teacher head0.220
Teacher spread0.092 · 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
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

Citations2
Published2022
Admission routes1
Has abstractyes

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