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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 is currently being investigated for replacing the conflict resolution task of Air Traffic Control is Reinforcement Learning. As violation of the required separation margins, also called an intrusion, is an event of relatively low frequency, using Reinforcement Learning for this task comes with difficulties that can potentially be attributed to data imbalance. This paper artificially increased the traffic density during the training phase of the Reinforcement Learning method to investigate what the importance is of a balanced data set on the performance of the Reinforcement Learning method. It was found that as the traffic density increased, the Reinforcement Learning methods started to outperform the analytical methods. Beyond this it was found that methods trained at higher traffic densities, but tested at lower traffic densities, outperformed the methods trained at that specific density. This indicates that it might be better to always ensure that the training scenarios are more complex than anticipated during the execution phase, even if that results in unrealistic scenarios., Control & Simulation

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 conflict resolution methods. One of the main disadvantages of analytical conflict resolution methods, in high-traffic density scenarios, is that they can cause instabilities of the airspace due to a domino effect of secondary conflicts. Therefore, many studies have also investigated other methods of conflict resolution, such as Deep Reinforcement Learning, which have shown positive results, but tend to be hard to explain due to their black-box nature. This paper investigates if it is possible to explain the behaviour of a Soft Actor-Critic model, trained for resolving vertical conflicts in a layered urban airspace, by interpreting the policy through a heat map of the selected actions. It was found that the model actively changes its policy depending on the degrees of freedom and has a tendency to adopt preventive behaviour on top of conflict resolution. This behaviour can be directly linked to a decrease in secondary conflicts when compared to analytical methods and can potentially be incorporated into these methods to improve them while maintaining explainability., Air Transport & Operations, Control & Simulation

The interest in urban air mobility as a potential solution for urban congestion is steadily growing. Air operations in urban areas can present added complexity as compared with traditional air traffic management. As a result, it is necessary to test and develop novel airspace designs and rules. As airspace in urban areas is a scarce resource, creating structures and rules that effectively utilise the airspace is an important challenge. This work specifically focuses on layered airspace design in urban operations constrained to fly between the existing buildings. Two design parameters of airspace design are investigated with two sub-experiments. Sub-experiment 1 investigates layer function assignment by comparing concepts from previous research with different layer assignment distributions. Sub-experiment 2 investigates the flight rules of vertical distribution of traffic within the airspace, to determine whether this is best achieved in a static (pre-allocated) or dynamic manner. Both sub-experiments analyse the overall system safety, route duration, and route distance under increasing traffic demand. Results reveal that the importance of cruising airspace is apparent at high densities. Results also shows that the safest layer allocation flight rule depends on the traffic density. At lower densities dynamic rules help to spread traffic locally. However, when the airspace is saturated it is safer to pre-allocate flight heights if achieved uniformly., Control & Simulation

Unmanned Aircraft Systems (UAS) Traffic Management (UTM) is an active research subject as its proposed applications are increasing. UTM aims to enable a variety of UAS operations, including package delivery, infrastructure inspection, and emergency missions. That creates the need for extensive research on how to incorporate such traffic, as conventional methods and operations used in Air Traffic Management (ATM) are not suitable for constrained urban airspace. This paper proposes and compares several traffic capacity balancing methods developed for a UTM system designed to be used in highly dense, very low-level urban airspace. Three types of location-based dynamic traffic capacity management techniques are tested: street-based, grid-based, and cluster-based. The proposed systems are tested by simulating traffic within mixed (constrained and open) urban airspace based on the city of Vienna at five different traffic densities. Results show that using local, area-based clustering for capacity balancing within a UTM system improves safety, efficiency, and capacity metrics, especially when simulated or historical traffic data are used., Control & Simulation

The traffic density of small aerial vehicles operating within urban environments is expected to increase significantly in the near future. This urban environment is highly constrained due to being limited to the low-altitude airspace directly above the existing road network. Multiple studies have addressed factors influencing the capacity of urban airspace. These have used simulations of aircraft, yet the empirical nature of these simulations limits their use beyond the specific conditions that have been tested. Analytical models would not have this limitation, but they are only developed for general airspace, while the emergent patterns in constrained urban airspace are different than in general, unconstrained conditions. For instance, queuing and local congestion are patterns that are unique to the heavily-constrained environment. Therefore, in this paper, we derive an analytical model for air traffic in a confined airspace to find the influencing factors for its capacity. By means of a simulation of aerial vehicles, we verify the analytical model and show a relationship between the mean flow rate and mean density in a two-dimensional orthogonal grid network airspace. Results show that the entire airspace can become unstable when the maximum capacity of just one intersection is reached. Furthermore, the maximum airspace density is found to be unaffected by cruise speed. The results demonstrate how the derived analytical model provides an effective tool to predict the effect of several design parameters on the capacity of constrained urban airspace. Moreover, this model can form the basis for further extensions, including the altitude dimension and non-orthogonal or non-four-way intersections., Control & Simulation, Transport and Planning

The use of small aircraft for a wide range of missions in urban airspace is expected to increase in the future. In Europe, efforts have been invested into developing a unified system, called U-space, to manage aircraft in dense very-low-level urban airspace. The Metropolis II project aimed to research what degree of centralisation an air traffic management system should use in such airspace. The paper at hand is a follow-up, and investigates improvements that can be brought to the tactical conflict prevention, detection, and resolution module of such a system in order to harmonise these components with an organic high-density U-space environment. The proposed improvements are: the prioritisation of vertical conflict prevention in intersections, the use of intent in detecting and resolving conflicts, and the use of heading-based manoeuvres in open airspace. Results show that the use of intent information in the conflict detection process, as well as the implementation of suitable tactical prevention procedures, can greatly increase airspace safety. Furthermore, the experiments revealed that the effectiveness of conflict resolution algorithms is highly dependent on the airspace rules and structure. This reiterates the potential for increasing the safety and efficiency of operations within constrained airspace if the tactical separation modules are unified with the other components of air traffic management systems for U-space., Control & Simulation

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 included in the state, which introduces human biases. Another option that has been used is Recurrent Neural Networks to process the entire sequence of aircraft present. These methods however are sequence-dependent and can give different results depending on the order that the aircraft are given, which is undesirable. Methods that solely rely on attention mechanisms, such as transformers, allow sequential data to be processed in a sequence-invariant manner by using multi-head attention mechanisms. However, because traditional Transformers operate on individual tokens, this does not allow for relative state information to be encoded into the hidden state. This paper shows that by performing a transformation operation on the key and value tokens, it is possible to use Transformers on relative states, at the cost of a factor (N-1) additional attention computations, where N is the number of agents in the system. This adaptation allows relative state Transformers to obtain significantly higher performance than standard Transformers. The results also showed that using attention mechanisms to construct the initial observation vector out of a total of 20 agents results in similar, but slightly lower, performance to handcrafted observation vectors, without requiring manual selection of the important agents. Future research should investigate whether additional changes to the attention mechanisms and their training can result in higher performance., Control & Simulation

The interest for using small aircraft for missions in urban airspace is growing for applications like parcel deliveries. Research shows that conventional airspace structure and conflict detection and resolution techniques are not suitable for main- taining a high level of safety in constrained urban environments, especially when aircraft are restricted to flying within the limits of the road network. The problem at hand becomes even more complex when factoring in cities with topologically organic street networks, thus increasing the probability of crossing and merging traffic flows. Preliminary results show that such networks induce the detection of false-positive conflicts when using classical state- based conflict detection, decreasing the effectiveness of conflict resolution. Velocity-obstacle based conflict resolution methods were able to improve airspace safety, but require further de- velopment in order to handle conflicts in such an unpredictable and constrained environment. Thus, the doctoral project at hand seeks to develop and research improved methods for conflict prevention, detection and resolution in constrained, urban, very low-level urban airspace., Control & Simulation

The rate of urbanization is expected to continue increasing. This has led to an interest in using drones and air taxis for urban transportation in place of the current methods, which often lead to road congestion. In most places urban air operations will happen above buildings. However, in many cities with large skyscrapers it may not be efficient to fly above buildings as it would add travel distance. For these cases, aircraft will have to operate in constrained airspace (above roads and between buildings). There is still a knowledge gap for operating in constrained very low-level urban airspace. Most studies attempt to improve the safety in constrained airspace with strategic or tactical conflict resolution. But this may not be enough to ensure safety in highly-dense urban environments. The restriction of heading manoeuvres by buildings substantially limits the solution space for conflict resolution. Therefore, conflict prevention with airspace design can be an important tool for improving airspace safety. In a layered airspace, turn layers can be used so that turning aircraft do not create bottlenecks for cruising aircraft that may be behind it. However, merging conflicts can occur when these turning aircraft attempt to re-enter cruising layers. These are typical in both orthogonal (New York) and non-orthogonal (Paris) street networks. Non-orthogonal street networks can also create merging conflicts because it is not always possible to segment cruising aircraft at intersections. This work will propose two conflict prevention doctoral research experiments that aim to reduce merging conflicts. The first will use three different layering techniques to reduce merging conflicts created by turn layers. The second will focus on merging conflicts that are typical of non-orthogonal networks., Control & Simulation

In order to enable the safe and efficient integration of Unmanned Aerial Vehicles into very low level airspace, current day research focuses on the development of new traffic services and procedures. One of these is the geovectoring protocol, which aims to reduce traffic complexity by setting limits on the allowed ground speed, course, and vertical speed. A geovector can be used to increase the capacity of an airspace by lowering the conflict rate. However, problems emerge when performing avoidance maneuvers in geovectored airspace, as the limits are ignored in this process. A powerful conflict resolution algorithm is the Modified Voltage Potential (MVP). This paper proposes an extension to the MVP algorithm, based on Velocity Obstacle theory. Making use of an alternative horizontal conflict resolution maneuver which respects the geovector, five resolution strategies are defined with different priority settings for the separate limits. The performance of these strategies is compared to pure MVP on geovector, safety, and stability measures, making use of fast-time simulations in a corridor airspace. All geovector resolution strategies show improvements on the ability to perform conflict resolution maneuvers within the geovector limits, while having marginal effects on the overall airspace safety level. It is recommended to further investigate the performance of the geovector resolution strategies for other types of airspace, to verify whether the observed reduction in conflict rate from the geovectors can be reinforced by the resolution strategies., Control & Simulation

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 required to have detect and avoid capabilities. At higher traffic densities these avoidance manoeuvres can, however, lead to instabilities within the airspace, causing emergent patterns that lead to knock-on effects that can harm the safety of the operations. It might be possible to formulate a cost function that encapsulates global safety, rather than individual safety, stimulating both safety and stability. One method that lends itself for optimizing such a cost function is cooperative Multi-Agent Reinforcement Learning (MARL). It has been demonstrated that MARL can be used for optimization in both competitive and cooperative (or even mixed) environments, however, when applied in a completely decentralized manner, stability issues often arise. It is therefore proposed to investigate the application of MARL for a well known centralized domain, ATC for manned aviation. This doctoral paper breaks down the proposed research project into 4 independent phases that individually contribute to the knowledge of applying MARL for ATC., Control & Simulation

The Metropolis II project aimed to study the impact of centralised separation management for urban aerial mobility. Three concepts were developed in this study: a fully centralised, strategically separated concept, a hybrid concept featuring centralised strategic separation and distributed tactical separation, and a fully distributed tactical concept. A comparative simulation study was performed, using traffic scenarios based on predicted demand in an urban airspace in the city of Vienna. Simulations were performed with varying traffic densities and situations. Results show that the purely strategic and purely tactical strategies perform comparably in terms of safety, and that further improvements can be achieved with a combination of those strategies.

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., Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Control & Simulation

The Metropolis II project aimed to study the impact of centralised separation management for urban aerial mobility. Three concepts were developed in this study: a fully centralised, strategically separated concept, a hybrid concept featuring cen- tralised strategic separation and distributed tactical separation, and a fully distributed tactical concept. A comparative simu- lation study was performed, using traffic scenarios based on predicted demand in an urban airspace in the city of Vienna. Simulations were performed with varying traffic densities and situations. Results show that the purely strategic and purely tactical strategies perform comparably in terms of safety, and that further improvements can be achieved with a combination of those strategies., Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Control & Simulation

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 control problem can be effectively modelled as a Markov game, where a team of aircraft (the agents) interact in the airspace (the environment) and cooperatively take resolution actions to achieve a common goal: safe separation in the most efficient way. As in any Markov game, the optimal policy for the team could be learnt through trial and error in a simulated environment using reinforcement learning algorithms. In this paper, we use the soft actor-critic algorithm to unravel the optimal air traffic control policy. Unlike some previous works, we propose a global (i.e., shared) reward that encourages cooperative behaviour. Furthermore, we propose a versatile policy model capable of performing heading, speed, and/or altitude resolution actions. We also demonstrate that the policy is robust and can maintain safe separation even in the presence of uncertainty regarding aircraft position, delays in implementing resolution actions, and wind. The findings of this paper also suggest that there is still significant room for improvement when controlling three degrees of freedom at the same time., Control & Simulation

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., Control & Simulation

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., Control & Simulation

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., https://github.com/TUDelft-CNS-ATM/bluesky, Control & Simulation

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., Control & Simulation

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., Control & Simulation

Package delivery via autonomous drones is often presumed to hold commercial and societal value when applied to urban environments. However, to realise the benefits, the challenge of safely managing high traffic densities of drones in heavily constrained urban spaces needs to be addressed. This paper applies the principles of traffic segmentation and alignment to a constrained airspace in efforts to mitigate the probability of conflict. The study proposes an en-route airspace concept in which drone flights are directly guided along a one-way street network. This one-way airspace concept uses heading-altitude rules to vertically segment cruising traffic as well as transitioning flights with respect to their travel direction. However, transition flights trigger a substantial number of merging conflicts, thus negating a large part of the benefits gained from airspace structuring. In this paper, we aim to reduce the occurrence of merging conflicts and intrusions by using a delay-based and speed-based merge-assist strategy, both well-established methods from road traffic research. We apply these merge assistance strategies to the one-way airspace design and perform simulations for three traffic densities for the experiment area of Manhattan, New York. The results indicate, at most, a 9–16% decrease in total number of intrusions with the use of merge assistance. By investigating mesoscopic features of the urban street network, the data suggest that the relatively low efficacy of the merge strategies is mainly caused by insufficient space for safe manoeuvrability and the inability for the strategies to fully respond and thus resolve conflicts on short-distance streets., Control & Simulation, Control & Operations

Purpose of Review A lot of research into decentralised, state-based conflict detection and resolution, or detect and avoid algorithms has been executed. This paper explains the essential properties of state-based conflict detection and reviews the work in the context of applications for not only manned but also unmanned aerial vehicles, where this might be applied relatively soon. Recent Findings Lately, based on several reviews of a variety of published algorithms, a selection has been implemented and simulated in extremely high traffic densities for comparison. Summary The modified voltage potential has been surprisingly efficient, even compared with more complex algorithms or adaptations, as is apparent from looking at macroscopic metrics like domino effect, efficiency and safety. This indicates that to this date, it is so far the most suitable algorithm for the detect and avoid role for unmanned aerial vehicles in urban airspaces, or other areas where a high density is expected., Control & Operations, Control & Simulation

Drone-based delivery is likely to reduce energy and greenhouse gas emissions associated with the transport of small express packages, fast-food meals and medicines, especially when deployed in large-scale in urban areas. However, it is an enormous challenge to facilitate such high traffic densities in constrained urban environments. A recent study applied the principles of traffic alignment and segmentation to the constrained urban airspace of Manhattan, New York, in an effort to mitigate conflict probability. In that study, two novel airspace concepts were proposed, namely, the two-way and one-way concept. Both concepts employed a heading-altitude rule to vertically segmented traffic with respect to their travel directions. In addition, the one-way concept also featured horizontal constraints to promote unidirectional traffic flow. These concepts bear resemblance to that of road-based traffic. Further, both concepts featured transition altitudes to accommodate turning flights that need to decelerate to safely perform turns at intersections. The comparative study showed the one-way airspace configuration to be more effective than the two-way concept in terms of safety. In the pursuit of demonstrating our understanding of the intricate differences between the two-way and one-way airspace configurations, this paper aims to explore and analyse salient conflict properties. By using fast-time simulation experiments, the different types of conflicts are captured and analysed for multiple traffic demand levels. Our results suggest that conflicts are largely caused by flights climbing or descending to their respective altitude layers. For both concepts, the merging conflicts consisted of in-trail and crossing conflicts, while the two-way also contained a high proportion of head-on conflicts. Our study therefore sheds light on the different categories of conflicts that could help guide future research in airspace design in constrained urban areas., Control & Simulation, Control & Operations

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., Control & Simulation, Control & Operations

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., Control & Simulation, Control & Operations

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. 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, Control & Simulation, Control & Operations

Involving air traffic controllers and pilots into the bird strike prevention process is considered an essential step to increase aviation and avian safety. Prior to implementing operational measures such as real-time warning systems, it is vital to evaluate their feasibility. This paper studies the efficacy of a bird strike advisory system for air traffic control. In addition to the potential safety benefit, the possible impact on airport operations is analyzed. To this end, a previously developed collision avoidance algorithm underlying the system was tested in fast-time Monte Carlo simulations involving various air traffic and bird densities to obtain representative conclusions for different operational conditions. The results demonstrate the strong safety potential of operational bird strike prevention in case of precise bird movement prediction. Unless airports operate close to their capacity limits while bird abundance is high, the induced delays remain tolerable. Prioritization of hazardous strikes involving large individuals as well as flocks of birds are expected to support operational feasibility in all conditions, Control & Simulation, Control & Operations

Modern aircraft can be equipped with a flight envelope protection system: automation which modifies pilot control inputs to ensure that the aircraft remains within the allowable limits. Overruling the pilot inputs may lead to mode confusion, even when visual or auditory feedback is provided to alert pilots. We advocate using active control devices to make the flight envelope protection system tangible to the pilot. This paper presents the main findings of an evaluation of three haptic feedback designs for flight envelope protection. The first concept used both force feedback and vibro-tactile alerts, producing promising, yet inconclusive, results. The second concept used asymmetric vibrations to give directional alerting cues, which did not result in improved performance on initial use, but which did yield improved learning rate for the task. The third system employed force feedback to physically guide the pilot away from flight envelope limits, which yielded safety improvements from the first use, but created dependence: pilot performance degraded immediately after the force feedback was removed. From this, we advise to use asymmetric vibrations during training for flight envelope excursions, to leverage active control interfaces for providing force feedback during operation, and reevaluate a combination of both to combine their advantages for single-pilot operations., Control & Simulation, Human-Robot Interaction

Modern aircraft use a variety of fly-by-wire control devices and combine these with a flight envelope protection system to limit pilot control inputs when approaching the aircraft limits. The current research project aims to increase pilot awareness of such a protection system through the use of force feedback on the control device, i.e., haptics. A previous design used asymmetric vibrations to cue the pilot on the flight envelope. The evaluation showed no improvement in metrics at the first emergency encounter, yet did show a potential training benefit. Therefore, a new haptic feedback concept was designed with the specific aim to guide the pilot when approaching a limit and provide support from the first time use. This paper evaluates these haptic feedback designs with 36 active PPL/LAPL pilots who flew a challenging vertical profile and encountered a windshear in a fixed-base simulator. The pilots were divided in three groups who received either cueing, guidance, or no haptic feedback. It was hypothesized that: (i) cueing haptic feedback provides a faster learning rate compared to no-haptics, and (ii) guidance haptic feedback results in best performance from the first run yet worse metrics when no feedback is provided. Comparing the results of the cueing and no-haptic feedback groups confirmed the first hypothesis. Results also showed that the guidance haptic feedback resulted in improved metrics at the first run, and the worsening of metrics when no longer provided., Virtual/online event due to COVID-19, Control & Simulation, Human-Robot Interaction