The paper develops a deep neural network- (DNN) based mass transport approach to cover a distributed target in a decentralized manner by Uncrewed Aerial Systems (UAS). This is a new decentralized UAS transport approach with time-varying communication weights that can be achieved by solving the following three sub-problems: (i) determining the DNN structure, (ii) obtaining communication weights, and (iii) ensuring stability and convergence guarantee. By proposing a novel algorithmic approach, the DNN is structured based on the UAS initial formation with an arbitrary distribution in the motion space. To specify communication weights for a team of $N$ multi-copters, we use the DNN to obtain the initial communication weights, based on the agents' initial positions, abstractly represent the distributed target by $N$ points, considered as the final positions of all agents, and obtain the final communication weights. The third sub-problem is to prove the stability and convergence of the UAS transport.


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    Title :

    Deep Neural Network-Based UAS Transport


    Contributors:


    Publication date :

    2025-05-14


    Size :

    1046297 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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