This paper addresses a path planning problem for unmanned aerial vehicles with correcting position errors through correction-point navigation, which requires a rapid response when determining the flight path. A two-layer nested iterative hybrid algorithm based on Q learning is proposed to achieve multiobjective optimization by minimizing path lengths and correction times while reducing the complexity of the algorithm and improving the efficiency of path planning. The lower-layer algorithm uses a Q -learning framework based on the experience playback mechanism and exploration/exploitation mechanism. The rewards and punishments for the Q -table values in the lower-layer algorithm are innovatively managed with the unit “path.” The upper-layer algorithm is based on the Pareto multiobjective algorithm. The Pareto frontier is continuously updated with the solutions from the lower-layer algorithm, which timely provides the lower-layer algorithm feedback. Finally, simulation experiments are conducted to evaluate the effectiveness of the algorithm, and the results show that the proposed algorithm outperforms particle swarm optimization.


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    Titel :

    Fast Path Planning for Unmanned Aerial Vehicles by Self-Correction Based on Q -Learning


    Beteiligte:
    Wang, Ziyang (Autor:in) / Yang, Hongbing (Autor:in) / Wu, Qingsong (Autor:in) / Zheng, Jiafei (Autor:in)

    Erschienen in:

    Erscheinungsdatum :

    2021-01-29


    Format / Umfang :

    9 pages




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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