Searching indoor environments in the presence of unknown obstacles with multiple UAV agents remains a challenge. This paper presents a framework for target-finding using a combination of traditional POMDP based planning and Deep Reinforcement Learning. The implementation breaks the problem into two separate stages of planning and control, with both stages modelled as a Partially Observable Markov Decision Process (POMDP). Global decentralised planning is provided using a modern online POMDP solver, while a modern Deep Reinforcement Learning algorithm is used to provide a policy for local control. Our results indicate that such a framework is capable of target-finding within a simulated indoor test environment in the presence of unknown obstacles, and once extended to real-world operation could enable UAVs to be applied in an increasing number of applications.


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    Title :

    Multi-UAV Target-Finding in Simulated Indoor Environments using Deep Reinforcement Learning


    Contributors:


    Publication date :

    2020-03-01


    Size :

    8224728 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English




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