The prevalence of Unmanned Aerial Vehicles (UAVs) in precision agriculture has been growing rapidly. This paper tackles the UAV global mission planning problem by incorporating a greater capacity for human-machine teaming in the architecture of a flexibly autonomous, near-fully-distributed Mission Management System for UAV swarms. Subsequently, the two problems of global mission planning are solved simultaneously using an integrated solution. This consists of a geometric clustering algorithm which prioritizes the minimization of overall mission time, and an off-policy, model-free Temporal Difference Learning global agent capable of learning about an initially unknown mission environment through simulations. The latter component makes the solution adaptive to missions with different requirements.


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    Title :

    Adaptive UAV Swarm Mission Planning by Temporal Difference Learning




    Publication date :

    2021-10-03


    Size :

    2338127 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English





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