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.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

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


    Additional title:

    Lect. Notes in Networks, Syst.



    Conference:

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



    Publication date :

    2022-11-19


    Size :

    12 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




    Multi-Agent Task Allocation with Interagent Distance Constraints

    Choi, Euihyeon / Chang, Woohyuk | AIAA | 2024


    Task allocation in cooperative air combat based on multi-agent coalition

    Xinghua, D. / Yangwang, F. / Bingsong, X. et al. | British Library Online Contents | 2014


    Dynamic task scheduling in flight simulators

    Kurtzberg, J.M. | Engineering Index Backfile | 1963


    An overview of agent-based traffic simulators

    Johannes Nguyen / Simon T. Powers / Neil Urquhart et al. | DOAJ | 2021

    Free access

    Adaptive Task Allocation for Multi-agent Cooperation with Unknown Capabilities

    Li, Jialun / Li, Yushan / Weng, Yulai et al. | IEEE | 2020