Navigating multiple drones autonomously in complex and unpredictable environments, such as forests, poses a significant challenge typically addressed by wireless communication for coordination. However, this approach falls short in situations with limited central control or blocked communications. Addressing this gap, our paper explores the learning of complex behaviors by multiple drones with limited vision. Drones in a swarm rely on onboard sensors, primarily forward-facing stereo cameras, for environmental perception and neighbor detection. They learn complex maneuvers through the imitation of a privileged expert system, which involves finding the optimal set of neural network parameters to enable the most effective mapping from sensory perception to control commands. The training process adopts the Dagger algorithm, employing the framework of centralized training with decentralized execution. Using this technique, drones rapidly learn complex behaviors, such as avoiding obstacles, coordinating movements, and navigating to specified targets, all in the absence of wireless communication. This paper details the construction of a distributed multi-UAV cooperative motion model under limited vision, emphasizing the autonomy of each drone in achieving coordinated flight and obstacle avoidance. Our methodological approach and experimental results validate the effectiveness of the proposed vision-based end-to-end controller, paving the way for more sophisticated applications of multi-UAV systems in intricate, real-world scenarios.


    Zugriff

    Download


    Exportieren, teilen und zitieren



    Titel :

    Imitation Learning of Complex Behaviors for Multiple Drones with Limited Vision


    Beteiligte:
    Yu Wan (Autor:in) / Jun Tang (Autor:in) / Zipeng Zhao (Autor:in)


    Erscheinungsdatum :

    2023




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Unbekannt




    Adversarial Learning Improves Vision-Based Perception from Drones with Imbalanced Datasets

    Dolph, Chester V. / Ippolito, Corey / Glaab, Louis J. et al. | AIAA | 2023



    Imitation learning for vision-based lane keeping assistance

    Innocenti, Christopher / Linden, Henrik / Panahandeh, Ghazaleh et al. | IEEE | 2017


    VISION ENHANCED DRONES FOR PRECISION FARMING

    BLEIWEISS AMIT | Europäisches Patentamt | 2017

    Freier Zugriff

    Vision enhanced drones for precision farming

    BLEIWEISS AMIT | Europäisches Patentamt | 2018

    Freier Zugriff