The multi-robot task allocation (MRTA) systems face the challenge of adapting to dynamic environments where new tasks and communication errors might appear during execution. This paper presents a framework to run agent-based MRTA within a physical simulator to test different algorithms and/or setups. Agents are modeled by a specific type of state machines able to represent deliberative behaviors as well as reactivity. While this adds formality and simplifies implementation, execution of state machines within a physical simulator requires decoupling transitions that imply the passing of time from those occurring instantly. The result framework includes a state machine execution engine that synchronizes with the simulator’s engine. Experiments using an auction-based MRTA for an example plant show not only the capability of the framework for modeling a wide range of systems but also that the MRTA method works with on-the-fly task inclusions, varying number of active robots and error occurrences.


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    Titel :

    Integrating State-Based Multi-Agent Task Allocation and Physical Simulators


    Weitere Titelangaben:

    Lect. Notes in Networks, Syst.


    Beteiligte:
    Tardioli, Danilo (Herausgeber:in) / Matellán, Vicente (Herausgeber:in) / Heredia, Guillermo (Herausgeber:in) / Silva, Manuel F. (Herausgeber:in) / Marques, Lino (Herausgeber:in) / Rivas, Daniel (Autor:in) / Ribas-Xirgo, Lluís (Autor:in)

    Kongress:

    Iberian Robotics conference ; 2022 ; Zaragoza, Spain November 23, 2022 - November 25, 2022



    Erscheinungsdatum :

    19.11.2022


    Format / Umfang :

    12 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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