  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.
