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Hybrid fuzzy response threshold-based distributed task allocation in heterogeneous multi-robot environment.

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Název: Hybrid fuzzy response threshold-based distributed task allocation in heterogeneous multi-robot environment.
Autoři: Joseph, Dani Reagan Vivek, Ramapackiyam, Shantha Selvakumari
Zdroj: Journal of Ambient Intelligence & Smart Environments; 2024, Vol. 16 Issue 4, p465-484, 20p
Témata: AMBIENT intelligence, FUZZY algorithms, FUZZY logic, FUZZY systems, ROBOTS
Abstrakt: Task allocation is a vital challenge in a multi-robot environment. A hybrid fuzzy response threshold-based method is proposed to address the problem of task allocation in a heterogeneous mobile robot environment. The method follows a distributed task allocation approach where every robot chooses its task and performs it, resulting in concurrent execution. The algorithm uses a fuzzy inference system to determine the capability of the robot to carry out a task. Then, the robot employs the response threshold model, utilizing the obtained capability to decide on the task to complete. The objective here is to maximize the tasks completed with the resources available while balancing the affinity with which the task is done. The proposed algorithm is initially applied to the static scenario where there is no failure among the mobile robots. The algorithm is then improved to run in the dynamic scenario to study the effect on the allocation. The proposed algorithm is empirically evaluated in simulation for multiple runs under different environment instances. The results show a good increase in tasks performed successfully across all the instances in static and dynamic scenarios. The proposed algorithms are validated using FireBird V mobile robots in an experimental environment. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Ambient Intelligence & Smart Environments is the property of Sage Publications Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Databáze: Complementary Index
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IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Hybrid fuzzy response threshold-based distributed task allocation in heterogeneous multi-robot environment.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Joseph%2C+Dani Reagan Vivek%22">Joseph, Dani Reagan Vivek</searchLink><br /><searchLink fieldCode="AR" term="%22Ramapackiyam%2C+Shantha Selvakumari%22">Ramapackiyam, Shantha Selvakumari</searchLink>
– Name: TitleSource
  Label: Source
  Group: Src
  Data: Journal of Ambient Intelligence & Smart Environments; 2024, Vol. 16 Issue 4, p465-484, 20p
– Name: Subject
  Label: Subject Terms
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22AMBIENT+intelligence%22">AMBIENT intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22FUZZY+algorithms%22">FUZZY algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22FUZZY+logic%22">FUZZY logic</searchLink><br /><searchLink fieldCode="DE" term="%22FUZZY+systems%22">FUZZY systems</searchLink><br /><searchLink fieldCode="DE" term="%22ROBOTS%22">ROBOTS</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Task allocation is a vital challenge in a multi-robot environment. A hybrid fuzzy response threshold-based method is proposed to address the problem of task allocation in a heterogeneous mobile robot environment. The method follows a distributed task allocation approach where every robot chooses its task and performs it, resulting in concurrent execution. The algorithm uses a fuzzy inference system to determine the capability of the robot to carry out a task. Then, the robot employs the response threshold model, utilizing the obtained capability to decide on the task to complete. The objective here is to maximize the tasks completed with the resources available while balancing the affinity with which the task is done. The proposed algorithm is initially applied to the static scenario where there is no failure among the mobile robots. The algorithm is then improved to run in the dynamic scenario to study the effect on the allocation. The proposed algorithm is empirically evaluated in simulation for multiple runs under different environment instances. The results show a good increase in tasks performed successfully across all the instances in static and dynamic scenarios. The proposed algorithms are validated using FireBird V mobile robots in an experimental environment. [ABSTRACT FROM AUTHOR]
– Name: Abstract
  Label:
  Group: Ab
  Data: <i>Copyright of Journal of Ambient Intelligence & Smart Environments is the property of Sage Publications Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.3233/AIS-230196
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      – Code: eng
        Text: English
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      Pagination:
        PageCount: 20
        StartPage: 465
    Subjects:
      – SubjectFull: AMBIENT intelligence
        Type: general
      – SubjectFull: FUZZY algorithms
        Type: general
      – SubjectFull: FUZZY logic
        Type: general
      – SubjectFull: FUZZY systems
        Type: general
      – SubjectFull: ROBOTS
        Type: general
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      – TitleFull: Hybrid fuzzy response threshold-based distributed task allocation in heterogeneous multi-robot environment.
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            NameFull: Joseph, Dani Reagan Vivek
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            NameFull: Ramapackiyam, Shantha Selvakumari
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            – D: 01
              M: 12
              Text: 2024
              Type: published
              Y: 2024
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            – TitleFull: Journal of Ambient Intelligence & Smart Environments
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