A method to maximize the total coverage of multiple unmanned aerial vehicles (UAVs) which monitor a bounded space is presented. The goal of all UAVs is to maximize their individual coverage while minimize possible coverage overlaps among them. This goal is achieved using a multi-agent reinforcement learning (MARL) method which is embedded with a coordination strategy that allows several UAVs to negotiate their actions to avoid possible overlaps between their coverage. Simulation results are shown to illustrate the developed MARL scheme's performance.


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    Title :

    Area Coverage Maximization of Multi UAVs Using Multi-Agent Reinforcement Learning


    Contributors:


    Publication date :

    2023-12-14


    Size :

    547795 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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