论文标题
使用深钢筋学习技术的自动免费领空控制器
An Autonomous Free Airspace En-route Controller using Deep Reinforcement Learning Techniques
论文作者
论文摘要
由于飞机数量的增加，空中交通管制正在成为越来越复杂的任务。当前的空中交通管制方法不适合管理这种增加的流量。自主空中交通管制被认为是一种有希望的替代方案。在本文中，提出了一个空中交通管制模型，该模型指导了三维无组织空域中任意数量的飞机，同时避免了冲突和碰撞。这是利用基于图的深度学习方法的力量完成的。这些方法比当前执行此任务的方法具有很大的优势，例如对飞机输入顺序的不变性以及轻松应对不同数量的飞机的能力。使用这些方法获得的结果表明，空中交通管制模型在逼真的交通密度上表现良好。它能够通过避免100％的潜在碰撞并防止89.8％的潜在冲突来管理领空。
Air traffic control is becoming a more and more complex task due to the increasing number of aircraft. Current air traffic control methods are not suitable for managing this increased traffic. Autonomous air traffic control is deemed a promising alternative. In this paper an air traffic control model is presented that guides an arbitrary number of aircraft across a three-dimensional, unstructured airspace while avoiding conflicts and collisions. This is done utilizing the power of graph based deep learning approaches. These approaches offer significant advantages over current approaches to this task, such as invariance to the input ordering of aircraft and the ability to easily cope with a varying number of aircraft. Results acquired using these approaches show that the air traffic control model performs well on realistic traffic densities; it is capable of managing the airspace by avoiding 100% of potential collisions and preventing 89.8% of potential conflicts.
