Unmanned Aerial Vehicles (UAVs) embodied intelligence is of increasingly important since UAVs are well suited to perform tasks that are dangerous or impractical for humans. Numerous model-based approaches for autonomous UAV exploration have been proposed, yet they predominantly rely on a single sensor. This reliance limits the range of information obtained and diminishes the success rate in search missions. In this paper, in order to tackle those issues, a multimodal UAV autonomous exploration system is proposed. A pioneering method is introduced that harnesses deep reinforcement learning to synergize spectral data with LiDAR information, thereby significantly enhancing the navigation capabilities of UAVs in complex environments. A novel UAV equipped with spectral sensing and conducted extensive experiments is developed on a virtual simulation platform. Simulation results demonstrate that the proposed method demonstrating increased robustness and a higher success rate in obstacle avoidance.


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    Titel :

    Toward Embodied Intelligence: An Autonomous Exploration System for Multimodal UAV


    Beteiligte:
    Hu, Zihan (Autor:in) / Yang, Weikai (Autor:in) / Zhang, Meng (Autor:in) / Lei, Chengdong (Autor:in) / Liang, Hongtao (Autor:in) / Zhou, Fuhui (Autor:in) / Wu, Qihui (Autor:in)


    Erscheinungsdatum :

    05.07.2024


    Format / Umfang :

    2239467 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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