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Spontaneous Emergence of Multitasking Robotic Swarms

2022· article· en· W4317384010 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 IEEE International Conference on Robotics and Biomimetics (ROBIO) · 2022
Typearticle
Languageen
FieldComputer Science
TopicNonlinear Dynamics and Pattern Formation
Canadian institutionsDalhousie University
FundersChina Postdoctoral Science Foundation
KeywordsRobotHuman multitaskingSwarm behaviourComputer scienceSynchronization (alternating current)Swarm roboticsEdge of chaosDistributed computingArtificial intelligenceResource (disambiguation)SimulationBiologyNeuroscience

Abstract

fetched live from OpenAlex

Robot swarms promise to replace humans in complex scenarios like resource exploration, environmental monitoring, and military missions. These complex scenarios require robots to be able to complete multiple tasks at the same time adaptively. From the point of view of statistical mechanics, the phenomenon of the multiply states of a given system is a kind of partial synchronization. Inspired by biological groups such as flocks of birds and schools of fish, we model the robots as self-propelled particles with Kuramoto-Sakaguchi like interactions. We uncover the state of the robot swarm can be manipulated using the phase lag of the Kuramoto-Sakaguchi like interactions. The system is partially synchronized at the edge between order and disorder, and the robots present two distinguished motion patterns, one is completely periodic movement, and the other one is chaos. This study provides new insights into the collaboration of robot swarms.

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)0.000
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: Empirical · Consensus signal: none
Teacher disagreement score0.843
Threshold uncertainty score0.688

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 science0.0010.000
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 head0.038
GPT teacher head0.278
Teacher spread0.240 · 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