This paper presents a centralized receding discrete time horizon controller for use in cooperative high level unmanned air vehicle (UAV) motion planning during a search for land based targets over large ground regions with sensor occlusions. Due to the non-Markovian, dynamic, multimodal, and discontinuous objective function employed, the controller presented uses a stochastic optimizer in the form of either a particle swarm optimizer (PSO) or a comprehensive learning PSO (CLPSO) to calculate UAV routes. A hybrid maneuver automaton is applied to model vehicle dynamics and a Fisher information theory based model of discrete ground regions is used as an objective function. Finally, the non-Markovian process PSO (NMPPSO) algorithm is introduced to achieve better performance by customizing the PSO and its variants to decision processes with non-Markovian reward through the use of a suffix trie.


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    Title :

    The application of particle swarm optimization and maneuver automatons during non-Markovian motion planning for air vehicles performing ground target search


    Contributors:
    Martin, S.R. (author) / Newman, A.J. (author)


    Publication date :

    2008


    Size :

    6 Seiten, 21 Quellen




    Type of media :

    Conference paper


    Type of material :

    Print


    Language :

    English




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