论文标题
鹦鹉：增强学习和轨迹知识所推动的预测临时路由
PARRoT: Predictive Ad-hoc Routing Fueled by Reinforcement Learning and Trajectory Knowledge
论文作者
论文摘要
利用临时网络技术协调其行动的无人驾驶飞机（UAV）的群体提供了催化新兴研究领域的潜力，例如对灾害区域的自主探索，需求驱动的网络提供，以及智能运输系统中的近距离域名包装。由于这些移动机器人网络的特征是相对移动性的高等级，因此现有的路由协议通常无法对隐含的网络拓扑动态采用其决策。为了应对这些挑战，我们提出了通过强化学习和轨迹知识（Parrot）作为一种新型机器学习的路由协议所推动的预测临时路由，该路由协议利用了移动性控制信息，以将有关移动代理的未来运动的知识整合到路由过程中。使用综合网络模拟评估了建议的路由方法的性能。与已建立的路由协议相比，鹦鹉实现了较高的鲁棒性和明显较低的端到端潜伏期。
Swarms of collaborating Unmanned Aerial Vehicles (UAVs) that utilize ad-hoc networking technologies for coordinating their actions offer the potential to catalyze emerging research fields such as autonomous exploration of disaster areas, demanddriven network provisioning, and near field packet delivery in Intelligent Transportation Systems (ITSs). As these mobile robotic networks are characterized by high grades of relative mobility, existing routing protocols often fail to adopt their decision making to the implied network topology dynamics. For addressing these challenges, we present Predictive Ad-hoc Routing fueled by Reinforcement learning and Trajectory knowledge (PARRoT) as a novel machine learning-enabled routing protocol which exploits mobility control information for integrating knowledge about the future motion of the mobile agents into the routing process. The performance of the proposed routing approach is evaluated using comprehensive network simulation. In comparison to established routing protocols, PARRoT achieves a massively higher robustness and a significantly lower end-to-end latency.
