Collective Perception of Environmental Features in a Robot Swarm

Valentini, Gabriele; Brambilla, Davide; Hamann, Heiko; Dorigo, Marco

Collective Perception of Environmental Features in a Robot Swarm

Dateien

Valentini_2-3jq4w629qmt77.pdfGröße: 608.41 KBDownloads: 4

Datum

2016

Autor:innen

Valentini, Gabriele

Brambilla, Davide

Hamann, Heiko

Dorigo, Marco

Open Access-Veröffentlichung

Open Access Green

Sammlungen

Informatik und Informationswissenschaft

Publikationstyp

Beitrag zu einem Konferenzband

Publikationsstatus

Published

Erschienen in

DORIGO, Marco, ed., Mauro BIRATTARI, ed., Xiaodong LI, ed., Manuel LÓPEZ-IBÁÑEZ, ed., Kazuhiro OHKURA, ed., Carlo PINCIROLI, ed., Thomas STÜTZLE, ed.. Swarm Intelligence : 10th International Conference, ANTS 2016, Brussels, Belgium, September 7-9, 2016, Proceedings. Cham: Springer, 2016, pp. 65-76. Lecture Notes in Computer Science. 9882. ISSN 0302-9743. eISSN 1611-3349. ISBN 978-3-319-44426-0. Available under: doi: 10.1007/978-3-319-44427-7_6

Zusammenfassung

In order to be effective, collective decision-making strategies need to be not only fast and accurate, but sufficiently general to be ported and reused across different problem domains. In this paper, we propose a novel problem scenario, collective perception, and use it to compare three different strategies: the DMMD, DMVD, and DC strategies. The robots are required to explore their environment, estimate the frequency of certain features, and collectively perceive which feature is the most frequent. We implemented the collective perception scenario in a swarm robotics system composed of 20 e-pucks and performed robot experiments with all considered strategies. Additionally, we also deepened our study by means of physics-based simulations. The results of our performance comparison in the collective perception scenario are in agreement with previous results for a different problem domain and support the generality of the considered strategies.

Fachgebiet (DDC)

004 Informatik

Schlagwörter

Quality Estimate, Dissemination State, Voter Model, Exploration State, Swarm Size

Konferenz

ANTS 2016 : 10th International Conference on Swarm Intellingence, 7. Sept. 2016 - 9. Sept. 2016, Brussels, Belgium

Zitieren

ISO 690

VALENTINI, Gabriele, Davide BRAMBILLA, Heiko HAMANN, Marco DORIGO, 2016. Collective Perception of Environmental Features in a Robot Swarm. ANTS 2016 : 10th International Conference on Swarm Intellingence. Brussels, Belgium, 7. Sept. 2016 - 9. Sept. 2016. In: DORIGO, Marco, ed., Mauro BIRATTARI, ed., Xiaodong LI, ed., Manuel LÓPEZ-IBÁÑEZ, ed., Kazuhiro OHKURA, ed., Carlo PINCIROLI, ed., Thomas STÜTZLE, ed.. Swarm Intelligence : 10th International Conference, ANTS 2016, Brussels, Belgium, September 7-9, 2016, Proceedings. Cham: Springer, 2016, pp. 65-76. Lecture Notes in Computer Science. 9882. ISSN 0302-9743. eISSN 1611-3349. ISBN 978-3-319-44426-0. Available under: doi: 10.1007/978-3-319-44427-7_6

BibTex

@inproceedings{Valentini2016Colle-66016,
  year={2016},
  doi={10.1007/978-3-319-44427-7_6},
  title={Collective Perception of Environmental Features in a Robot Swarm},
  number={9882},
  isbn={978-3-319-44426-0},
  issn={0302-9743},
  publisher={Springer},
  address={Cham},
  series={Lecture Notes in Computer Science},
  booktitle={Swarm Intelligence : 10th International Conference, ANTS 2016, Brussels, Belgium, September 7-9, 2016, Proceedings},
  pages={65--76},
  editor={Dorigo, Marco and Birattari, Mauro and Li, Xiaodong and López-Ibáñez, Manuel and Ohkura, Kazuhiro and Pinciroli, Carlo and Stützle, Thomas},
  author={Valentini, Gabriele and Brambilla, Davide and Hamann, Heiko and Dorigo, Marco}
}

RDF

<rdf:RDF
    xmlns:dcterms="http://purl.org/dc/terms/"
    xmlns:dc="http://purl.org/dc/elements/1.1/"
    xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#"
    xmlns:bibo="http://purl.org/ontology/bibo/"
    xmlns:dspace="http://digital-repositories.org/ontologies/dspace/0.1.0#"
    xmlns:foaf="http://xmlns.com/foaf/0.1/"
    xmlns:void="http://rdfs.org/ns/void#"
    xmlns:xsd="http://www.w3.org/2001/XMLSchema#" > 
  <rdf:Description rdf:about="https://kops.uni-konstanz.de/server/rdf/resource/123456789/66016">
    <foaf:homepage rdf:resource="http://localhost:8080/"/>
    <dcterms:title>Collective Perception of Environmental Features in a Robot Swarm</dcterms:title>
    <dc:rights>terms-of-use</dc:rights>
    <dspace:hasBitstream rdf:resource="https://kops.uni-konstanz.de/bitstream/123456789/66016/1/Valentini_2-3jq4w629qmt77.pdf"/>
    <bibo:uri rdf:resource="https://kops.uni-konstanz.de/handle/123456789/66016"/>
    <dc:contributor>Valentini, Gabriele</dc:contributor>
    <dc:contributor>Dorigo, Marco</dc:contributor>
    <dcterms:isPartOf rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/36"/>
    <dcterms:hasPart rdf:resource="https://kops.uni-konstanz.de/bitstream/123456789/66016/1/Valentini_2-3jq4w629qmt77.pdf"/>
    <dcterms:rights rdf:resource="https://rightsstatements.org/page/InC/1.0/"/>
    <dc:language>eng</dc:language>
    <dc:creator>Hamann, Heiko</dc:creator>
    <dc:creator>Brambilla, Davide</dc:creator>
    <dc:creator>Dorigo, Marco</dc:creator>
    <dc:contributor>Hamann, Heiko</dc:contributor>
    <dc:contributor>Brambilla, Davide</dc:contributor>
    <dc:creator>Valentini, Gabriele</dc:creator>
    <dspace:isPartOfCollection rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/36"/>
    <dc:date rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2023-02-09T12:26:25Z</dc:date>
    <dcterms:available rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2023-02-09T12:26:25Z</dcterms:available>
    <dcterms:abstract xml:lang="eng">In order to be effective, collective decision-making strategies need to be not only fast and accurate, but sufficiently general to be ported and reused across different problem domains. In this paper, we propose a novel problem scenario, collective perception, and use it to compare three different strategies: the DMMD, DMVD, and DC strategies. The robots are required to explore their environment, estimate the frequency of certain features, and collectively perceive which feature is the most frequent. We implemented the collective perception scenario in a swarm robotics system composed of 20 e-pucks and performed robot experiments with all considered strategies. Additionally, we also deepened our study by means of physics-based simulations. The results of our performance comparison in the collective perception scenario are in agreement with previous results for a different problem domain and support the generality of the considered strategies.</dcterms:abstract>
    <dcterms:issued>2016</dcterms:issued>
    <void:sparqlEndpoint rdf:resource="http://localhost/fuseki/dspace/sparql"/>
  </rdf:Description>
</rdf:RDF>

Universitätsbibliographie

Nein