The invention discloses an unmanned aerial vehicle double-layer conflict resolution method for an urban low-altitude dense traffic flow airspace, and the method comprises the steps: constructing a mixed integer nonlinear programming model based on a conflict resolution strategy before an intelligent unmanned aerial vehicle takes off, and optimizing the mixed integer nonlinear programming model through an improved random fractal search algorithm; after the intelligent unmanned aerial vehicle takes off, a dual depth Q network is constructed and network training is carried out on the dual depth Q network, and the trained dual depth Q network is utilized to enable the intelligent unmanned aerial vehicle to output conflict-free maneuvering actions at each time step. According to the method, a double-layer conflict resolution framework is provided in combination with deep reinforcement learning, and conflict resolution before flight and real-time conflict resolution are combined, so that the occurrence of air collision is greatly reduced compared with that of any method which is independently used, and the intelligent unmanned aerial vehicle can avoid the current obstacle in an ultra-low-altitude urban dense traffic flow environment, thereby improving the traffic flow resolution efficiency. And meanwhile, secondary conflicts are reduced, and the training time of the real-time conflict solving method based on reinforcement learning is also remarkably shortened.

    本发明公开了面向城市低空密集交通流空域的无人机双层冲突消解方法,包括:在智能无人机起飞前,构建基于冲突消解策略的混合整数非线性规划模型,利用改进的随机分形搜索算法优化混合整数非线性规划模型;在智能无人机起飞后,构建双重深度Q网络并对其进行网络训练,利用训练后的双重深度Q网络使智能无人机在每个时间步输出无冲突的机动动作。本发明结合深度强化学习提出了一个双层冲突消解框架,结合飞行前冲突解除与实时冲突解决,不仅相比单独使用任何一种方法大幅减少了空中碰撞的发生,使智能无人机可以在超低空城市密集交通流环境下通过躲避当前障碍物,同时减少二次冲突,还显著缩短了基于强化学习的实时冲突解决方法的训练时间。


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    Titel :

    Urban low-altitude dense traffic flow airspace-oriented unmanned aerial vehicle double-layer conflict resolution method


    Weitere Titelangaben:

    面向城市低空密集交通流空域的无人机双层冲突消解方法


    Beteiligte:
    YAN CHAO (Autor:in) / ZHANG MINGCHENG (Autor:in) / CHEN MOU (Autor:in)

    Erscheinungsdatum :

    13.05.2025


    Medientyp :

    Patent


    Format :

    Elektronische Ressource


    Sprache :

    Chinesisch


    Klassifikation :

    IPC:    G06N COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS , Rechnersysteme, basierend auf spezifischen Rechenmodellen / G08G Anlagen zur Steuerung, Regelung oder Überwachung des Verkehrs , TRAFFIC CONTROL SYSTEMS



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