Hierarchical decision-making is a natural paradigm for coordinating multi-agent systems in complex environments such as air traffic management. In this paper, we present a bilevel framework for game-theoretic hierarchical routing, where a high-level router assigns discrete routes to multiple vehicles who seek to optimize potentially noncooperative objectives that depend upon the assigned routes. To address computational challenges, we propose a reformulation that preserves the convexity of each agent's feasible set. This convex reformulation enables a solution to be identified efficiently via a customized branch-and-bound algorithm. Our approach ensures global optimality while capturing strategic interactions between agents at the lower level. We demonstrate the solution concept of our framework in two-vehicle and three-vehicle routing scenarios.

Urban Air Mobility (UAM) promises a new dimension to decongested, safe, and fast travel in urban and suburban hubs. These UAM aircraft are conceived to operate from small airports called vertiports each comprising multiple take-off/landing and battery-recharging spots. Since they might be situated in dense urban areas and need to handle many aircraft landings and take-offs each hour, managing this schedule in real-time becomes challenging for a traditional air-traffic controller but instead calls for an automated solution. This paper provides a novel approach to this problem of Urban Air Mobility - Vertiport Schedule Management (UAM-VSM), which leverages graph reinforcement learning to generate decision-support policies. Here the designated physical spots within the vertiport's airspace and the vehicles being managed are represented as two separate graphs, with feature extraction performed through a graph convolutional network (GCN). Extracted features are passed onto perceptron layers to decide actions such as continue to hover or cruise, continue idling or take-off, or land on an allocated vertiport spot. Performance is measured based on delays, safety (no. of collisions) and battery consumption. Through realistic simulations in AirSim applied to scaled down multi-rotor vehicles, our results demonstrate the suitability of using graph reinforcement learning to solve the UAM-VSM problem and its superiority to basic reinforcement learning (with graph embeddings) or random choice baselines.

To minimize collision risks in the multi-agent path planning problem with stochastic transition dynamics, we formulate a Markov decision process congestion game with a multi-linear congestion cost. Players within the game complete individual tasks while minimizing their own collision risks. We show that the set of Nash equilibria coincides with the first-order KKT points of a non-convex optimization problem. Our game is applied to a historical flight plan over France to reduce collision risks between commercial aircraft.

Imagine flying from Europe to Australia in just 90 minutes. This is fantasy for now, but the stratosphere is the next frontier in aviation, with supersonic flights using that high-altitude space. And one of the keys to making it happen will be the use of Artificial Intelligence (AI) to cope with the increased complexity the sector will face. "Aviation is being reshaped by a number of powerful forces that are fundamentally impacting the Air Traffic Management sector," says Beatrice Pesquet-Popescu, Research and Business Innovation Director for Air Traffic Management (ATM) at Thales. "In addition to the growth expected in traditional aircraft, we will have to cope with new vehicles such as drones and stratospheric balloons, circulating in low or high altitude airspace."

The air traffic management service NATS has begun a trial to understand whether Artificial Intelligence (AI) could be used to help reduce flight delays. A project is now underway, within NATS' bespoke Digital Tower Laboratory, at Heathrow Airport to test whether a combination of ultra HD 4K cameras along with state-of-the-art AI and machine learning technology can be used to help improve the airport's landing capacity in times of low visibility and improve punctuality. Heathrow's 87 metre tall control tower is the highest in the UK and provides commanding views of the airport and surrounding landscape, but its height can also mean it disappears into low cloud, even when the runways below are clear. In those conditions, where the controllers have to rely on radar to know if an arriving aircraft has left the runway, extra time is given between each landing to ensure its safety. The result is a 20% loss of landing capacity, which creates delays for passengers and knock-on disruption for the rest of the operation.

The UAE's aviation authority has signed an agreement which will explore the use of artificial intelligence (AI) in the country's air traffic management. The UAE General Civil Aviation Authority (GCAA) and Searidge Technologies have signed the deal to cooperate in pursuing research and development activities. The collaboration is the first in the region and will bring together the technical expertise of Canada-based Searidge Technologies in digital airport solutions and the operational expertise of GCAA, a statement said. Saif Mohammed Al Suwaidi, director general of the GCAA, confirmed that it is the UAE's strategy to explore new technologies and applications in the aviation industry. "We look forward to working with Searidge and believe this collaboration will help advance the use of technology in aviation to optimize safety and efficiency in the UAE and around the globe. The MoU agreement will promote, develop and reinforce administrative, technical and scientific cooperation," added Ahmed Al Jallaf, assistant director general Air Navigation Services, GCAA.

Advances in deep technology, machine learning and automation are ushering a new era of digital workers. In the near future, drones, artificial intelligence and driverless cars will seamlessly coordinate and transport goods and people across the globe at rather smaller cost. In fact, drones in particular have caught the interest of several bodies and policymakers across the globe. Countries across the world are exploring the possibilities of drones and their extent of usage in different scenarios. From delivering online grocery orders at the doorstep, to providing emergency medical supplies to remote areas, or facilitating unmanned surveillance in dangerous warzones, there are many more ways in which Unmanned Aerial Vehicles (UAVs) or drones are changing the commerce landscape as well as our lives.

If you fly your drone as a hobby and paid a fee to register it with the Federal Aviation Administration, you can now get a refund. In 2015, the FAA placed a rule that required owners who operated their drones for fun to register their small aircraft. In May, a U.S. Appeals Court in the D.C. circuit said the FAA drone registration violated a 2012 law passed by Congress. Section 336 of the FAA Modernization and Reform Act says the administration "may not promulgate any rule or regulation regarding a model aircraft." Because the ruling in May, the FAA announced this week it will refund the $5 people paid to register their drones.

In aviation industry, free flight is a new concept which implies considering more freedom in the selection and modification of flight paths during flight time. The free flight concept allows pilots choose their own flight paths more efficient, and also plan for their flight with high performance. Although free flight has many advantages such as minimum delays and the reduction of the workload of the air traffic control centers, this concept causes many problems which one of the most important of them are conflicts between different aircrafts. Thus, Conflict Detection and Resolution (CD&R) is a major challenge in air traffic management. In this paper, we presented a model for CD&R between aircrafts in air traffic management using Graph Coloring Problem (GCP) method. In fact, we mapped the congestion area to a corresponding graph, and then addressed to find a reliable and optimal coloring for this graph using one of the new evolutionary algorithms known as Imperialist Competitive Algorithm (ICA) to solve the conflicts. Using ICA for solving GCP is a new method.

Using constraint-based local search, we effectively model and efficiently solve the problem of balancing the traffic demands on portions of the European airspace while ensuring that their capacity constraints are satisfied. The traffic demand of a portion of airspace is the hourly number of flights planned to enter it, and its capacity is the upper bound on this number under which air-traffic controllers can work. Currently, the only form of demand-capacity balancing we allow is ground holding, that is the changing of the take-off times of not yet airborne flights. Experiments with projected European flight plans of the year 2030 show that already this first form of demand-capacity balancing is feasible without incurring too much total delay and that it can lead to a significantly better demand-capacity balance.