Evidence map›Paper›PMID 33501074›Full record

ArticleFrontiers in robotics and AI2019

Automatic Off-Line Design of Robot Swarms: A Manifesto.

Mauro Birattari, Antoine Ligot, Darko Bozhinoski, Manuele Brambilla, Gianpiero Francesca, Lorenzo Garattoni, David Garzón Ramos, Ken Hasselmann, Miquel Kegeleirs, Jonas Kuckling and 4 more

Abstract read
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Article in Frontiers in robotics and AI, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

15 citing papers in PubMed.

  1. Article
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  3. Article
  4. Review
  5. Article
  6. Article
  7. Article
  8. Swarm SLAM: Challenges and Perspectives.Frontiers in robotics and AI · 2021
    Review
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

14 authors.

Mauro BirattariUniversité libre de Bruxelles, Brussels, Belgium.
Antoine LigotUniversité libre de Bruxelles, Brussels, Belgium.
Darko BozhinoskiUniversité libre de Bruxelles, Brussels, Belgium.
Manuele BrambillaUniversité libre de Bruxelles, Brussels, Belgium.
Gianpiero FrancescaUniversité libre de Bruxelles, Brussels, Belgium.
Lorenzo GarattoniUniversité libre de Bruxelles, Brussels, Belgium.
David Garzón RamosUniversité libre de Bruxelles, Brussels, Belgium.
Ken HasselmannUniversité libre de Bruxelles, Brussels, Belgium.
Miquel KegeleirsUniversité libre de Bruxelles, Brussels, Belgium.
Jonas KucklingUniversité libre de Bruxelles, Brussels, Belgium.
Federico PagnozziUniversité libre de Bruxelles, Brussels, Belgium.
Andrea RoliAlma Mater Studiorum, Università di Bologna, Bologna, Italy.
Muhammad SalmanUniversité libre de Bruxelles, Brussels, Belgium.
Thomas StützleUniversité libre de Bruxelles, Brussels, Belgium.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Designing collective behaviors for robot swarms is a difficult endeavor due to their fully distributed, highly redundant, and ever-changing nature. To overcome the challenge, a few approaches have been proposed, which can be classified as manual, semi-automatic, or automatic design. This paper is intended to be the manifesto of the automatic off-line design for robot swarms. We define the off-line design problem and illustrate it via a possible practical realization, highlight the core research questions, raise a number of issues regarding the existing literature that is relevant to the automatic off-line design, and provide guidelines that we deem necessary for a healthy development of the domain and for ensuring its relevance to potential real-world applications.

Indexed as

automatic designcollective behaviorsdesign methodologyevolutionary roboticsswarm robotics

Identifiers

PMID33501074
PMCPMC7806002

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.