The purpose of this study is to explore the application of Deep learning (DL) in autonomous flight path planning of UA V, and improve the autonomous navigation and flight ability of UA V in complex environment. In terms of methods, this article first constructs a DL model to extract environmental information from sensor data carried by UA V. The model adopts the structure of Convolutional neural network (CNN), and identifies obstacles and safe flight areas through training and learning. Then, combined with the optimization ability of traditional path planning algorithm, using the perceptual information provided by DL model, an optimized path from the start point to the end point is planned for UA V. Experimental results show that the proposed algorithm can effectively plan a safe and efficient flight path for UA V in various complex environments. Compared with other path planning methods, the proposed algorithm has obvious advantages in path length, flight time and obstacle avoidance ability. In addition, the success rate and security of the algorithm have also been verified, showing the great potential of DL in UA V path planning.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Unmanned Aerial Vehicle Autonomous Flight Path Planning Algorithm Based on Deep Learning


    Contributors:
    Li, Shengpeng (author) / Qian, Yuhan (author)


    Publication date :

    2024-05-29


    Size :

    1296729 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Multiple Unmanned Aerial Vehicle Autonomous Path Planning Algorithm Based on Whale-Inspired Deep Q-Network

    Wenshan Wang / Guoyin Zhang / Qingan Da et al. | DOAJ | 2023

    Free access

    Deep learning for unmanned aerial vehicle transportation cost and path planning

    Hu, Shilin / Li, Zonglin / Wu, Fangyi et al. | SPIE | 2022


    SECURE UNMANNED AERIAL VEHICLE FLIGHT PLANNING

    CARRAWAY PRECIA / DANIALI ALI | European Patent Office | 2020

    Free access

    Unmanned Aerial Vehicles Path Planning Based on Deep Reinforcement Learning

    Wang, Guoqiu / Zheng, Xuanyu / Zhao, Haitong et al. | Springer Verlag | 2019


    Secure unmanned aerial vehicle flight planning

    CARRAWAY PRECIA / DANIALI ALI | European Patent Office | 2023

    Free access