Publikation:

Collective Decision-Making with Bayesian Robots in Dynamic Environments

Lade...
Vorschaubild

Dateien

Pfister_2-f2qln61a3qvs4.pdf
Pfister_2-f2qln61a3qvs4.pdfGröße: 593.08 KBDownloads: 10

Datum

2022

Autor:innen

Pfister, Kai

Herausgeber:innen

Kontakt

ISSN der Zeitschrift

Electronic ISSN

ISBN

Bibliografische Daten

Verlag

Schriftenreihe

Auflagebezeichnung

ArXiv-ID

Internationale Patentnummer

Angaben zur Forschungsförderung

Projekt

Open Access-Veröffentlichung
Open Access Green
Core Facility der Universität Konstanz

Gesperrt bis

Titel in einer weiteren Sprache

Publikationstyp
Beitrag zu einem Konferenzband
Publikationsstatus
Published

Erschienen in

2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). Piscataway, NJ: IEEE, 2022, S. 7245-7250. ISSN 2153-0858. eISSN 2153-0866. ISBN 978-1-66547-927-1. Verfügbar unter: doi: 10.1109/IROS47612.2022.9982019

Zusammenfassung

Collective decision-making enables self-organizing robot swarms to act autonomously on a swarm level and is essential to coordinate their actions as a whole. When robots only share and communicate information locally a distributed and decentralized approach is required. In a previous paper [4], an efficient method based on a distributed Bayesian algorithm was created to distinguish a binary environment. We extended it to have the capability of dealing with dynamic environments. Therefore, it must avoid global lock-in states. In many realistic applications the robot swarm needs to adapt to (collectively) measurable changes at runtime by revising previous collective decisions. The trade-off between decision-making speed and readiness to revise previous decisions is a seemingly unavoidable challenge. We present our extension of the former approach and study how this trade-off can efficiently be balanced.

Zusammenfassung in einer weiteren Sprache

Fachgebiet (DDC)
004 Informatik

Schlagwörter

Konferenz

IROS 2022 : IEEE/RSJ International Conference on Intelligent Robots and Systems, 23. Okt. 2022 - 27. Okt. 2022, Kyoto, Japan
Rezension
undefined / . - undefined, undefined

Forschungsvorhaben

Organisationseinheiten

Zeitschriftenheft

Zugehörige Datensätze in KOPS

Zitieren

ISO 690PFISTER, Kai, Heiko HAMANN, 2022. Collective Decision-Making with Bayesian Robots in Dynamic Environments. IROS 2022 : IEEE/RSJ International Conference on Intelligent Robots and Systems. Kyoto, Japan, 23. Okt. 2022 - 27. Okt. 2022. In: 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). Piscataway, NJ: IEEE, 2022, S. 7245-7250. ISSN 2153-0858. eISSN 2153-0866. ISBN 978-1-66547-927-1. Verfügbar unter: doi: 10.1109/IROS47612.2022.9982019
BibTex
@inproceedings{Pfister2022Colle-66285,
  year={2022},
  doi={10.1109/IROS47612.2022.9982019},
  title={Collective Decision-Making with Bayesian Robots in Dynamic Environments},
  isbn={978-1-66547-927-1},
  issn={2153-0858},
  publisher={IEEE},
  address={Piscataway, NJ},
  booktitle={2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
  pages={7245--7250},
  author={Pfister, Kai and Hamann, Heiko}
}
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/123456789/66285">
    <bibo:uri rdf:resource="https://kops.uni-konstanz.de/handle/123456789/66285"/>
    <dc:creator>Pfister, Kai</dc:creator>
    <dcterms:isPartOf rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/36"/>
    <dc:language>eng</dc:language>
    <void:sparqlEndpoint rdf:resource="http://localhost/fuseki/dspace/sparql"/>
    <dspace:hasBitstream rdf:resource="https://kops.uni-konstanz.de/bitstream/123456789/66285/1/Pfister_2-f2qln61a3qvs4.pdf"/>
    <dspace:isPartOfCollection rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/36"/>
    <dcterms:hasPart rdf:resource="https://kops.uni-konstanz.de/bitstream/123456789/66285/1/Pfister_2-f2qln61a3qvs4.pdf"/>
    <dcterms:available rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2023-03-03T12:13:44Z</dcterms:available>
    <dcterms:abstract xml:lang="eng">Collective decision-making enables self-organizing robot swarms to act autonomously on a swarm level and is essential to coordinate their actions as a whole. When robots only share and communicate information locally a distributed and decentralized approach is required. In a previous paper [4], an efficient method based on a distributed Bayesian algorithm was created to distinguish a binary environment. We extended it to have the capability of dealing with dynamic environments. Therefore, it must avoid global lock-in states. In many realistic applications the robot swarm needs to adapt to (collectively) measurable changes at runtime by revising previous collective decisions. The trade-off between decision-making speed and readiness to revise previous decisions is a seemingly unavoidable challenge. We present our extension of the former approach and study how this trade-off can efficiently be balanced.</dcterms:abstract>
    <foaf:homepage rdf:resource="http://localhost:8080/"/>
    <dcterms:title>Collective Decision-Making with Bayesian Robots in Dynamic Environments</dcterms:title>
    <dcterms:issued>2022</dcterms:issued>
    <dcterms:rights rdf:resource="https://rightsstatements.org/page/InC/1.0/"/>
    <dc:rights>terms-of-use</dc:rights>
    <dc:creator>Hamann, Heiko</dc:creator>
    <dc:contributor>Hamann, Heiko</dc:contributor>
    <dc:date rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2023-03-03T12:13:44Z</dc:date>
    <dc:contributor>Pfister, Kai</dc:contributor>
  </rdf:Description>
</rdf:RDF>

Interner Vermerk

xmlui.Submission.submit.DescribeStep.inputForms.label.kops_note_fromSubmitter

Kontakt
URL der Originalveröffentl.

Prüfdatum der URL

Prüfungsdatum der Dissertation

Finanzierungsart

Kommentar zur Publikation

Allianzlizenz
Corresponding Authors der Uni Konstanz vorhanden
Internationale Co-Autor:innen
Universitätsbibliographie
Ja
Begutachtet
Diese Publikation teilen