Analysis of the impact of traffic density on training of reinforcement learning based conflict resolution methods for drones

Conventional Air Traffic Control is still predominantly being done by human Air Traffic Controllers, however, as the traffic density increases, the workload of the controllers increases as well. Especially for the area of unmanned aviation, driven by the rise in drones, having human controllers might become unfeasible. One of the methods that...

Policy Analysis of Safe Vertical Manoeuvring using Reinforcement Learning: Identifying when to Act and when to stay Idle

The number of unmanned aircraft operating in the airspace is expected to grow exponentially during the next decades. This will likely lead to traffic densities that are higher than those currently observed in civil and general aviation, and might require both a different airspace structure compared to conventional aviation, as well as different...

Improving Safety of Vertical Manoeuvres in a Layered Airspace with Deep Reinforcement Learning

Current estimates show that the presence of unmanned aviation is likely to grow exponentially over the course of the next decades. Even with the more conservative estimates, these expected high traffic densities require a re-evaluation of the airspace structure to ensure safe and efficient operations. One structure that scored high on both the...

Lateral and Vertical Air Traffic Control Under Uncertainty Using Reinforcement Learning

Air traffic demand has increased at an unprecedented rate in the last decade (albeit interrupted by the COVID pandemic), but capacity has not increased at the same rate. Higher levels of automation and the implementation of decision-support tools for air traffic controllers could help increase capacity and catch up with demand. The air traffic...

Enabling Safe and Efficient Separation through Multi-Agent Reinforcement Learning

Over the next decades, it is expected that the number of unmanned aerial vehicles (UAVs) operating in the airspace will grow rapidly. Both the FAA (Federal Aviation Administration) and the ICAO (International Civil Aviation Organisation) have already stated that aircraft operating autonomously or beyond their operators’ line of sight are...

Using Relative State Transformer Models for Multi-Agent Reinforcement Learning in Air Traffic Control

Deep Reinforcement Learning has seen more usage in the field of Air Traffic Control over the last couple of years. As the number of aircraft in a given sector of airspace is not constant, there is a need for methods to be invariant to the number of agents in the system. Often this is done by making a selection of the aircraft that will be...