This paper addresses the dual challenges of obstacle avoidance and distributed tracking in networked Unmanned Aerial Vehicles (UAVs) by leveraging online planning techniques. Specifically, we introduce a trajectory planning framework for the leader UAV, which is based on the Ego-Planner and utilizes high-performance sensors alongside an advanced onboard computing system. The optimized trajectory of the leader UAV serves as a dynamic reference for the entire UAV network. Each low-cost follower UAV implements an online learning algorithm to extract high-order trajectory information from the leader, enabling the generation of feedforward control components. These feedforward components are integrated into a feedbackfeedforward control architecture, facilitating precise distributed tracking and effective obstacle avoidance. The proposed method significantly enhances cooperative UAV navigation in complex and obstacle-dense environments, demonstrating that low-cost UAVs can achieve reliable performance. Extensive simulations validate the approach's effectiveness, showing superior obstacle avoidance and tracking accuracy compared to conventional methods.


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    Titel :

    Obstacle Avoidance Distributed Tracking of Networked UAVs with Online Path Planning


    Beteiligte:
    Zhang, Qiang (Autor:in) / Qin, Kaiyu (Autor:in) / Chen, Bowen (Autor:in) / Tong, Shiji (Autor:in) / Yuan, Haoyang (Autor:in) / Lin, Boxian (Autor:in) / Shi, Mengji (Autor:in)


    Erscheinungsdatum :

    17.05.2025


    Format / Umfang :

    2479682 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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