In this, we presented a comprehensive methodology for an autonomous drone delivery system, focusing on efficient navigation, real-time obstacle avoidance, and precise control. The proposed system integrates Simultaneous Localization and Mapping (SLAM) for accurate localization within the environment and mapping of the environment, combined with the A* algorithm for optimal path planning. To enhance the system's capability to navigate dynamic environments, we utilize a deep learning-based model for real-time obstacle detection for collision avoidance. Furthermore, Model Predictive Control (MPC) is employed to fine-tune the drone's flight path, ensuring smooth and stable movement while adapting to environmental uncertainties. This multi-layered approach enables the drone to autonomously map its surroundings, compute the most efficient route, and safely avoid obstacles during delivery operations. The proposed method offers a robust solution for autonomous drone navigation in complex and unpredictable environments, making it highly suitable for drone delivery applications in various real-world scenarios.


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    Titel :

    Dronav: Deep Learning Based Autonomous Drone Navigation System


    Beteiligte:
    Darapureddy, Aditya (Autor:in) / Guravana, Ashok (Autor:in) / Daniya, T. (Autor:in) / Potunuru, Dinesh (Autor:in) / Rajesh, Boyina (Autor:in) / Vugiri, Amrutha (Autor:in)


    Erscheinungsdatum :

    06.11.2024


    Format / Umfang :

    520014 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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