Search

Future operations involving drones are expected to result in traffic densities that are orders of magnitude higher than any observed in manned aviation. Current geometric conflict resolution (CR) methods have proven to be very efficient at relatively moderate densities. However, at higher densities, performance is hindered by the unpredictable emergent behaviour from neighbouring aircraft. Reinforcement learning (RL) techniques are often capable of identifying emerging patterns through training in the environment. Although some work has started introducing RL to resolve conflicts and ensure separation between aircraft, it is not clear how to employ these methods with a higher number of aircraft, and whether these can compare to or even surpass the performance of current CR geometric methods. In this work, we employ an RL method for distributed conflict resolution; the method is completely responsible for guaranteeing minimum separation of all aircraft during operation. Two different action formulations are tested: (1) where the RL method controls heading, and speed variation; (2) where the RL method controls heading, speed, and altitude variation. The final safety values are directly compared to a state-of-the-art distributed CR algorithm, the Modified Voltage Potential (MVP) method. Although, overall, the RL method is not as efficient as MVP in reducing the total number of losses of minimum separation, its actions help identify favourable patterns to avoid conflicts. The RL method has a more preventive behaviour, defending in advance against nearby neighbouring aircraft not yet in conflict, and head-on conflicts while intruders are still far away.

International audience; Metropolis II aims to provide insights in what is needed to enable high-density urban air operations. It does this by investigating the foundation for U-space U3/U4 services. The final goal is to provide a unified approach for strategic deconfliction, tactical deconfliction, and dynamic capacity management. Highly-dense operations in constrained urban airspace will likely require a degree of complexity that does not exist in modern-day air traffic management. The expected high traffic demand will require a shared use of the airspace instead of assigning exclusive use of blocks of the airspace to some flights. A unified approach for traffic management is needed because at high-densities, airspace design, flight planning, and separation management become increasingly interdependent. Metropolis II builds upon the results of the first Metropolis project. Three concepts with a varying degree of centralisation will be compared using simulations. (1) The centralised concept will take a global approach for separation management. (2) The decentralised concept aims to give the individual agents separation responsibility. (3) The hybrid concept tries to combine a centralised strategic planning agent with a robust tactical separation strategy.

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 safety and efficiency metrics, as defined by the Metropolis project, is a layered airspace, where aircraft with an intended heading are assigned to a specific altitude layer. However, a problem arises once aircraft start to vertically traverse between these layers, leading to a large number of conflicts and intrusions. One way to potentially reduce the number of intrusions during these operations is by using conventional conflict resolution algorithms. These algorithms however have also been shown to lead to instabilities at higher traffic densities. As recent years have shown tremendous growth in the capabilities of Deep Reinforcement Learning, it is interesting to see how well these methods perform in the field of conflict resolution. This research investigates and compares the performance of multiple Soft Actor Critic models with the Modified Voltage Potential algorithm during vertical manoeuvres in a layered airspace. The final obtained performance of the trained models is comparable to that of the Modified Voltage Potential algorithm and in certain scenarios, the trained models even outperform the MVP algorithm. Overall, the results show that DRL can improve upon the current state of conflict resolution algorithms and provide new insight into the development of safe operations.

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 safety and efficiency metrics, as defined by the Metropolis project, is a layered airspace, where aircraft with an intended heading are assigned to a specific altitude layer. However, a problem arises once aircraft start to vertically traverse between these layers, leading to a large number of conflicts and intrusions. One way to potentially reduce the number of intrusions during these operations is by using conventional conflict resolution algorithms. These algorithms however have also been shown to lead to instabilities at higher traffic densities. As recent years have shown tremendous growth in the capabilities of Deep Reinforcement Learning, it is interesting to see how well these methods perform in the field of conflict resolution. This research investigates and compares the performance of multiple Soft Actor Critic models with the Modified Voltage Potential algorithm during vertical manoeuvres in a layered airspace. The final obtained performance of the trained models is comparable to that of the Modified Voltage Potential algorithm and in certain scenarios, the trained models even outperform the MVP algorithm. Overall, the results show that DRL can improve upon the current state of conflict resolution algorithms and provide new insight into the development of safe operations.

Future high traffic densities with drone operations are expected to exceed the number of aircraft that current air traffic control procedures can control simultaneously. Despite extensive research on geometric CR methods, at higher densities, their performance is hindered by the unpredictable emergent behaviour from surrounding aircraft. In response, research has shifted its attention to creating automated tools capable of generating conflict resolution (CR) actions adapted to the environment and not limited by man-made rules. Several works employing reinforcement learning (RL) methods for conflict resolution have been published recently. Although proving that they have potential, at their current development, the results of the practical implementation of these methods do not reach their expected theoretical performance. Consequently, RL applications cannot yet match the efficacy of geometric CR methods. Nevertheless, these applications can improve the set of rules that geometrical CR methods use to generate a CR manoeuvre. This work employs an RL method responsible for deciding the parameters that a geometric CR method uses to generate the CR manoeuvre for each conflict situation. The results show that this hybrid approach, combining the strengths of geometric CR and RL methods, reduces the total number of losses of minimum separation. Additionally, the large range of different optimal solutions found by the RL method shows that the rules of geometric CR method must be expanded, catering for different conflict geometries.

Current predictions on future drone operations estimate that traffic density orders of magnitude will be higher than any observed in manned aviation. Such densities redirect the focus towards elements that can decrease conflict rate and severity, with special emphasis on airspace structures, an element that has been overlooked within distributed environments in the past. This work delves into the impacts of different airspace structures in multiple traffic scenarios, and how appropriate structures can increase the safety of future drone operations in urban airspace. First, reinforcement learning was used to define optimal heading range distributions with a layered airspace concept. Second, transition layers were reserved to facilitate the vertical deviation between cruising layers and conflict avoidance. The effects of traffic density, non-linear routes, and vertical deviation between layers were tested in an open-source airspace simulation platform. Results show that optimal structuring catered to the current traffic scenario improves airspace usage by correctly segmenting aircraft according to their flight routes. The number of conflicts and losses of minimum separation was reduced versus using a single, uniform airspace structure for all traffic scenarios, thus enabling higher airspace capacity.

Current predictions for future operations with drones estimate traffic densities orders of magnitude higher than any observed in manned aviation. Such densities call for further research and innovation, in particular, into conflict detection and resolution without the need for human intervention. The layered airspace concept, where aircraft are separated per vertical layer according to their heading, has been widely researched and proven to increase traffic capacity. However, aircraft traversing between layers do not benefit from this separation and alignment effect. As a result, interactions between climbing/descending and cruising aircraft can lead to a large increase in conflicts and intrusions. This paper looks into ways of reducing the impact of vertical transitions within the environment. We test two reinforcement learning methods: a decision-making module and a control execution module. The former issues a lane change command based on the planned route. The latter performs operational control to coordinate the longitude and vertical movement of the aircraft for a safe merging manoeuvre. The results show that reinforcement learning is capable of optimising an efficient driving policy for layer change manoeuvres, decreasing the number of conflicts and losses of minimum separation compared to manually defined navigation rules.

The capacity of the current system of air traffic is rapidly reaching a limit with the increasing demand for air transportation. Expected future traffic densities not only make automated conflict detection and resolution a necessity, but also force a re-evaluation of coordination elements to decrease conflict rate and severity. It has been acknowledged that airspace structure plays a positive role by acting as a first layer of conflict protection, reducing the likelihood of aircraft meeting and, consequently, the likelihood of conflicts and losses of minimum separation. In the recent past, different airspace structures have been explored. Research shows that the layered airspace concept, where groups of aircraft with similar headings remain separated by cruising at different altitudes, increases airspace capacity. However, implementation of this concept often employs an evenly distributed heading range per vertical layer, which is not optimal for all traffic scenarios, since it may lead to unevenly distributed numbers of aircraft per layer. In this work, we use supervised learning to determine a heading range distribution per layer adapted to the current traffic. This method resulted in a reduction of both conflicts and losses of minimum separation when compared to an evenly distributed layers concept. Results show that conflicts prevention, with a structure which efficiently segments aircraft through the airspace, may have a greater impact on safety than applying conflict resolution methods.

Air traffic contributes to global warming through CO2 and non-CO2 effects, including the impact of NOx emissions on atmospheric ozone and methane, formation of contrails, and changes in the amount of stratospheric water vapour. The climate impact of non-CO2 effects is highly dependent on the background atmospheric conditions at the time and location of emission. Therefore, there is the potential of mitigating the climate impact of aviation by optimizing the aircraft trajectories. In the present paper, we focus on the properties of alternative trajectories which have the potential to minimize the climate impact of NOx emissions, under a multitude of weather patterns. This study aims at enhancing the understanding of the relation between NOx-climate impacts and routing strategies, by employing the European Center Hamburg general circulation model (ECHAM) and the Modular Earth Submodel System (MESSy) Atmospheric Chemistry (EMAC) model. To this end, we conduct 1-year simulations with the air traffic submodel AirTraf 2.0, coupled to the EMAC model. We optimize 85 European flights, considering the atmospheric conditions at the time and location of the flight, to calculate the expected climate impact from the emitted species through a set of prototype algorithmic Climate Change Functions (aCCFs). The mean flight altitudes of NOx-climate optimal trajectories showed seasonal and latitudinal dependencies. We found that the potential of reducing ozone effects from aviation NOx is subjected to a strong seasonal cycle, reaching a minimum in summer.

Road traffic delay and urban overcrowding are increasing rapidly all over the world. As a result, several companies have proposed the use of small unmanned aerial vehicles (sUAVs) as an alternative to road-based transportation. These small autonomous drones are expected to operate within a thin airspace band (Very Low Level) in high traffic densities in constrained urban environments. This presents a challenge for ensuring the safe separation and efficient routing of drone flights. Current research has made modest progress towards finding solutions for conflict detection and prevention in highly dense and constrained environments (e.g., in-between buildings). In this paper, the state of the art of urban airspace design and conflict prevention and resolution research are discussed, and their applications to constrained environments. Additionally, fasttime high-fidelity simulations of high-density traffic scenarios are used along a non-orthogonal city layout to identify bottlenecks in the performance of speed-based conflict resolution in a multilayered airspace structure. Results show that the current airspace structure and conflict detection and resolution concepts need to be refined to further reduce conflicts and intrusions that occur in constrained environments. First, additional measures must be adapted to further prevent conflicts during turning and merging. Second, conflict resolution manoeuvres must account for speed limits resulting in turn radii which do not cross physical boundaries. Finally, conflict detection needs to consider the topology of the streets to prevent false-positive conflicts and to prepare in advance for conflicts resulting from heading changes in non-linear streets.

The capacity of the current system of air traffic is rapidly reaching a limit with the increasing demand for air transportation. Expected future traffic densities not only make automated conflict detection and resolution a necessity, but also force a re-evaluation of coordination elements to decrease conflict rate and severity. It has been acknowledged that airspace structure plays a positive role by acting as a first layer of conflict protection, reducing the likelihood of aircraft meeting and, consequently, the likelihood of conflicts and losses of minimum separation. In the recent past, different airspace structures have been explored. Research shows that the layered airspace concept, where groups of aircraft with similar headings remain separated by cruising at different altitudes, increases airspace capacity. However, implementation of this concept often employs an evenly distributed heading range per vertical layer, which is not optimal for all traffic scenarios, since it may lead to unevenly distributed numbers of aircraft per layer. In this work, we use supervised learning to determine a heading range distribution per layer adapted to the current traffic. This method resulted in a reduction of both conflicts and losses of minimum separation when compared to an evenly distributed layers concept. Results show that conflicts prevention, with a structure which efficiently segments aircraft through the airspace, may have a greater impact on safety than applying conflict resolution methods.

Air traffic contributes to global warming through CO2 and non-CO2 effects, including the impact of NOx emissions on atmospheric ozone and methane, formation of contrails, and changes in the amount of stratospheric water vapour. The climate impact of non-CO2 effects is highly dependent on the background atmospheric conditions at the time and location of emission. Therefore, there is the potential of mitigating the climate impact of aviation by optimizing the aircraft trajectories. In the present paper, we focus on the properties of alternative trajectories which have the potential to minimize the climate impact of NOx emissions, under a multitude of weather patterns. This study aims at enhancing the understanding of the relation between NOx-climate impacts and routing strategies, by employing the European Center Hamburg general circulation model (ECHAM) and the Modular Earth Submodel System (MESSy) Atmospheric Chemistry (EMAC) model. To this end, we conduct 1-year simulations with the air traffic submodel AirTraf 2.0, coupled to the EMAC model. We optimize 85 European flights, considering the atmospheric conditions at the time and location of the flight, to calculate the expected climate impact from the emitted species through a set of prototype algorithmic Climate Change Functions (aCCFs). The mean flight altitudes of NOx-climate optimal trajectories showed seasonal and latitudinal dependencies. We found that the potential of reducing ozone effects from aviation NOx is subjected to a strong seasonal cycle, reaching a minimum in summer.

Road traffic delay and urban overcrowding are increasing rapidly all over the world. As a result, several companies have proposed the use of small unmanned aerial vehicles (sUAVs) as an alternative to road-based transportation. These small autonomous drones are expected to operate within a thin airspace band (Very Low Level) in high traffic densities in constrained urban environments. This presents a challenge for ensuring the safe separation and efficient routing of drone flights. Current research has made modest progress towards finding solutions for conflict detection and prevention in highly dense and constrained environments (e.g., in-between buildings). In this paper, the state of the art of urban airspace design and conflict prevention and resolution research are discussed, and their applications to constrained environments. Additionally, fasttime high-fidelity simulations of high-density traffic scenarios are used along a non-orthogonal city layout to identify bottlenecks in the performance of speed-based conflict resolution in a multilayered airspace structure. Results show that the current airspace structure and conflict detection and resolution concepts need to be refined to further reduce conflicts and intrusions that occur in constrained environments. First, additional measures must be adapted to further prevent conflicts during turning and merging. Second, conflict resolution manoeuvres must account for speed limits resulting in turn radii which do not cross physical boundaries. Finally, conflict detection needs to consider the topology of the streets to prevent false-positive conflicts and to prepare in advance for conflicts resulting from heading changes in non-linear streets.

Current investigations into urban aerial mobility, as well as the continuing growth of global air transportation, have renewed interest in conflict detection and resolution (CD&amp, R) methods. The use of drones for applications such as package delivery, would result in traffic densities that are orders of magnitude higher than those currently observed in manned aviation. Such densities do not only make automated conflict detection and resolution a necessity, but will also force a re-evaluation of aspects such as coordination vs. priority, or state vs. intent. This paper looks into enabling a safe introduction of drones into urban airspace by setting travelling rules in the operating airspace which benefit tactical conflict resolution. First, conflicts resulting from changes of direction are added to conflict resolution with intent trajectory propagation. Second, the likelihood of aircraft with opposing headings meeting in conflict is reduced by separating traffic into different layers per heading–altitude rules. Guidelines are set in place to make sure aircraft respect the heading ranges allowed at every crossed layer. Finally, we use a reinforcement learning agent to implement variable speed limits towards creating a more homogeneous traffic situation between cruising and climbing/descending aircraft. The effects of all of these variables were tested through fast-time simulations on an open source airspace simulation platform. Results showed that we were able to improve the operational safety of several scenarios.

Current investigations into urban aerial mobility, as well as the continuing growth of global air transportation, have renewed interest in Conflict Detection and Resolution (CD&R) methods. With the new applications of drones, and the implications of a profoundly different urban airspace, new demands are placed on such algorithms, further spurring new research. This paper presents a review of current CR methods for both manned and unmanned aviation. It presents a taxonomy that categorises algorithms in terms of their approach to avoidance planning, surveillance, control, trajectory propagation, predictability assumption, resolution manoeuvre, multi-actor conflict resolution, considered obstacle types, optimization, and method category. More than a hundred CR methods were considered, showing how most work on a tactical, distributed framework. To enable a reliable comparison between methods, this paper argues that an open and ideally common simulation platform, common test scenarios, and common metrics are required. This paper presents an overview of four CR algorithms, each representing a commonly used CR algorithm category. Both manned and unmanned scenarios were tested, through fast-time simulations on an open-source airspace simulation platform.

Current investigations into urban aerial mobility, as well as the continuing growth of global air transportation, have renewed interest in Conflict Detection and Resolution (CD&R) methods. With the new applications of drones, and the implications of a profoundly different urban airspace, new demands are placed on such algorithms, further spurring new research. This paper presents a review of current CR methods for both manned and unmanned aviation. It presents a taxonomy that categorises algorithms in terms of their approach to avoidance planning, surveillance, control, trajectory propagation, predictability assumption, resolution manoeuvre, multi-actor conflict resolution, considered obstacle types, optimization, and method category. More than a hundred CR methods were considered, showing how most work on a tactical, distributed framework. To enable a reliable comparison between methods, this paper argues that an open and ideally common simulation platform, common test scenarios, and common metrics are required. This paper presents an overview of four CR algorithms, each representing a commonly used CR algorithm category. Both manned and unmanned scenarios were tested, through fast-time simulations on an open-source airspace simulation platform.