Safety is the primary concern when it comes to air traffic. In-flight safety between Unmanned Aircraft Vehicles (UAVs) is ensured through pairwise separation minima, utilizing conflict detection and resolution methods. Existing methods mainly deal with pairwise conflicts, however due to an expected increase in traffic density, encounters with more than two UAVs are likely to happen. In this paper, we model multi-UAV conflict resolution as a multi-agent reinforcement learning problem. We implement an algorithm based on graph neural networks where cooperative agents can communicate to jointly generate resolution maneuvers. The model is evaluated in scenarios with 3 and 4 present agents. Results show that agents are able to successfully solve the multi-UAV conflicts through a cooperative strategy.

翻译：无人驾驶航空器(无人驾驶航空器)之间的飞行安全是通过对称分离微型系统,利用冲突探测和解决方法确保的。现有方法主要处理对称冲突,然而,由于交通密度预计会增加,可能会遇到超过两架无人驾驶航空器。在本文件中,我们将多架无人驾驶航空器的冲突解决模式作为多剂强化学习问题。我们采用基于图形神经网络的算法,合作人员可以进行沟通,以联合产生解析动作。模型在与现有3和4名代理人的假想中进行评估。结果显示,代理人能够通过合作战略成功解决多架无人驾驶航空器的冲突。