Guidelines for improving the robustness of swarm robotic systems through adjustment of network topology

Xinran Wang, David Green, Jan Carlo Barca

    Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

    Abstract

    Swarm Robotics (SR) is expected to have a significant impact on society over the next decade. One of the contributing factors is that swarms are robust. However, robustness has not gained sufficient attention in the context of robotic swarms. This study focuses on the swarm network to generate insights as to how network topologies can be controlled to improve the robustness of SR systems. More specifically, how removing key robots alters the network topology, thereby changing the performance of the swarm. Analyzing these changes provides possible guidelines to improve swarm robustness towards targeted interventions. The most important findings suggest that robustness can be increased by making the network topology: (1) provincial and decentralized in the middle phase of the swarming procedure in unimodal domains, (2) provincial and centralized during the same phase in multi-objective domains.

    Original languageEnglish
    Title of host publication2017 IEEE International Conference on Advanced Intelligent Mechatronics (AIM)
    EditorsOliver Sawodny
    Place of PublicationNew York NY USA
    PublisherIEEE, Institute of Electrical and Electronics Engineers
    Pages1399-1405
    Number of pages7
    ISBN (Electronic)9781509060009, 9781509059997, 9781509060016
    ISBN (Print)9781509059980
    DOIs
    Publication statusPublished - 21 Aug 2017
    EventIEEE/ASME International Conference on Advanced Intelligent Mechatronics 2017 - Munich, Germany
    Duration: 3 Jul 20177 Jul 2017
    http://www.aim2017.org/ (Conference website)
    http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=7999201 (Proceedings)

    Conference

    ConferenceIEEE/ASME International Conference on Advanced Intelligent Mechatronics 2017
    Abbreviated titleAIM 2017
    CountryGermany
    CityMunich
    Period3/07/177/07/17
    Internet address

    Cite this

    Wang, X., Green, D., & Barca, J. C. (2017). Guidelines for improving the robustness of swarm robotic systems through adjustment of network topology. In O. Sawodny (Ed.), 2017 IEEE International Conference on Advanced Intelligent Mechatronics (AIM) (pp. 1399-1405). [8014214] New York NY USA: IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/AIM.2017.8014214
    Wang, Xinran ; Green, David ; Barca, Jan Carlo. / Guidelines for improving the robustness of swarm robotic systems through adjustment of network topology. 2017 IEEE International Conference on Advanced Intelligent Mechatronics (AIM). editor / Oliver Sawodny. New York NY USA : IEEE, Institute of Electrical and Electronics Engineers, 2017. pp. 1399-1405
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    title = "Guidelines for improving the robustness of swarm robotic systems through adjustment of network topology",
    abstract = "Swarm Robotics (SR) is expected to have a significant impact on society over the next decade. One of the contributing factors is that swarms are robust. However, robustness has not gained sufficient attention in the context of robotic swarms. This study focuses on the swarm network to generate insights as to how network topologies can be controlled to improve the robustness of SR systems. More specifically, how removing key robots alters the network topology, thereby changing the performance of the swarm. Analyzing these changes provides possible guidelines to improve swarm robustness towards targeted interventions. The most important findings suggest that robustness can be increased by making the network topology: (1) provincial and decentralized in the middle phase of the swarming procedure in unimodal domains, (2) provincial and centralized during the same phase in multi-objective domains.",
    author = "Xinran Wang and David Green and Barca, {Jan Carlo}",
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    Wang, X, Green, D & Barca, JC 2017, Guidelines for improving the robustness of swarm robotic systems through adjustment of network topology. in O Sawodny (ed.), 2017 IEEE International Conference on Advanced Intelligent Mechatronics (AIM)., 8014214, IEEE, Institute of Electrical and Electronics Engineers, New York NY USA, pp. 1399-1405, IEEE/ASME International Conference on Advanced Intelligent Mechatronics 2017, Munich, Germany, 3/07/17. https://doi.org/10.1109/AIM.2017.8014214

    Guidelines for improving the robustness of swarm robotic systems through adjustment of network topology. / Wang, Xinran; Green, David; Barca, Jan Carlo.

    2017 IEEE International Conference on Advanced Intelligent Mechatronics (AIM). ed. / Oliver Sawodny. New York NY USA : IEEE, Institute of Electrical and Electronics Engineers, 2017. p. 1399-1405 8014214.

    Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

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    AB - Swarm Robotics (SR) is expected to have a significant impact on society over the next decade. One of the contributing factors is that swarms are robust. However, robustness has not gained sufficient attention in the context of robotic swarms. This study focuses on the swarm network to generate insights as to how network topologies can be controlled to improve the robustness of SR systems. More specifically, how removing key robots alters the network topology, thereby changing the performance of the swarm. Analyzing these changes provides possible guidelines to improve swarm robustness towards targeted interventions. The most important findings suggest that robustness can be increased by making the network topology: (1) provincial and decentralized in the middle phase of the swarming procedure in unimodal domains, (2) provincial and centralized during the same phase in multi-objective domains.

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    Wang X, Green D, Barca JC. Guidelines for improving the robustness of swarm robotic systems through adjustment of network topology. In Sawodny O, editor, 2017 IEEE International Conference on Advanced Intelligent Mechatronics (AIM). New York NY USA: IEEE, Institute of Electrical and Electronics Engineers. 2017. p. 1399-1405. 8014214 https://doi.org/10.1109/AIM.2017.8014214