This paper deals with the path planning problem of unmanned aerial vehicle (UAV) swarm in the dense-obstacle environment. A novel hierarchical path planning approach with two-level structure is proposed to obtain collision-free and smooth paths for UAV swarm. In the first level, an improved particle swarm optimization (PSO) method is proposed to generate a collision-free global optimal path to determine the overall movement orientation of UAV swarm. In the second level, the improved artificial potential field (APF) combined with consensus theory is used for local path planning of each UAV in the swarm with the turning points extracted from the global optimal path obtained previously as a series of destinations under the leader-follower formation control framework. Numerical simulations are implemented to prove the validity of our proposed algorithm.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Efficient Path Planning for UAV Swarm Under Dense Obstacle Environment


    Weitere Titelangaben:

    Lect. Notes Electrical Eng.


    Beteiligte:
    Wu, Meiping (Herausgeber:in) / Niu, Yifeng (Herausgeber:in) / Gu, Mancang (Herausgeber:in) / Cheng, Jin (Herausgeber:in) / Li, Menglei (Autor:in) / Zhao, Chunhui (Autor:in) / Hu, Jinwen (Autor:in) / Xu, Zhao (Autor:in) / Guo, Chubing (Autor:in) / Dou, Zengfa (Autor:in)

    Kongress:

    International Conference on Autonomous Unmanned Systems ; 2021 ; Changsha, China September 24, 2021 - September 26, 2021



    Erscheinungsdatum :

    18.03.2022


    Format / Umfang :

    10 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




    Efficient Path Planning for UAV Swarm Under Dense Obstacle Environment

    Li, Menglei / Zhao, Chunhui / Hu, Jinwen et al. | British Library Conference Proceedings | 2022


    Efficient Path Planning for UAV Swarm Under Dense Obstacle Environment

    Li, Menglei / Zhao, Chunhui / Hu, Jinwen et al. | TIBKAT | 2022


    Mission Planning for Heterogeneous UAVs in Obstacle-Dense Environment

    Yu, Xinyong / Wang, Lei / Gao, Xiaohua et al. | Springer Verlag | 2023