Periodic Reporting for period 2 - DACUS (Demand and Capacity Optimisation in U-space)
Período documentado: 2021-07-01 hasta 2022-12-31
The demand for autonomous flight operations is expected to increase rapidly over the next years in Europe. To face this challenge, the European Commission supports the development of the U-space highly automated and digitalized service framework. As demand for drones over populated areas explodes, there will be a need for limiting the number of operations. The future Demand and Capacity Balancing (DCB) process in the context of U-space shall assist concurrent flight planning by multiple drone operators to ensure the availability of access to airspace, the adequate balance between system capacity and demand of drone operations, and fair and prioritized access to airspace. DACUS designs and validates a service-oriented DCB process for drone traffic management that addresses all these challenges.
DACUS serves the overall goal of enabling a large number of simultaneous drone operations, keeping safety as the main driver of the acceptable maximum number of drone operations, and stimulating an environment of trust on economical perspectives by increasing the probability of conducting a drone mission as planned and agreed upon with the final customers. On the other hand, DACUS takes into consideration that the social acceptance of increasing air mobility at low altitudes is of utmost importance, especially in urban environments where a potentially high traffic volume enters in conflict with a high population density. DACUS ensures an equitable and reasonable distribution of drone traffic taking into account societal impact considerations as a pillar of the DCB decision-making process.
Five specific objectives are set to achieve the overall objective of designing a DCB for U-space:
1. Design a DCB process for drone traffic management, from strategic to tactical phase, guided by a new U-space performance scheme that includes the development of indicators for airspace capacity.
2. Develop innovative U-space services and enabling models to predict the expected demand, and to calculate the airspace capacity taking into account the safety and the societal impacts.
3. Define Very Low Level (VLL) airspace structure and rules for the urban environment as the boundary conditions for the implementation of the U-space DCB process.
4. Set the relation between the airspace capacity and the performances of the separation techniques, finding the optimal balance between onboard separation and the U-space tactical conflict resolution service.
5. Refine Communication, Navigation, and Surveillance (CNS) requirements in support of the separation techniques, with a focus on urban environments.
DACUS serves the overall goal of enabling a large number of simultaneous drone operations, keeping safety as the main driver of the acceptable maximum number of drone operations, and stimulating an environment of trust on economical perspectives by increasing the probability of conducting a drone mission as planned and agreed upon with the final customers. On the other hand, DACUS takes into consideration that the social acceptance of increasing air mobility at low altitudes is of utmost importance, especially in urban environments where a potentially high traffic volume enters in conflict with a high population density. DACUS ensures an equitable and reasonable distribution of drone traffic taking into account societal impact considerations as a pillar of the DCB decision-making process.
Five specific objectives are set to achieve the overall objective of designing a DCB for U-space:
1. Design a DCB process for drone traffic management, from strategic to tactical phase, guided by a new U-space performance scheme that includes the development of indicators for airspace capacity.
2. Develop innovative U-space services and enabling models to predict the expected demand, and to calculate the airspace capacity taking into account the safety and the societal impacts.
3. Define Very Low Level (VLL) airspace structure and rules for the urban environment as the boundary conditions for the implementation of the U-space DCB process.
4. Set the relation between the airspace capacity and the performances of the separation techniques, finding the optimal balance between onboard separation and the U-space tactical conflict resolution service.
5. Refine Communication, Navigation, and Surveillance (CNS) requirements in support of the separation techniques, with a focus on urban environments.
DACUS has elaborated a consolidated concept of operations for DCB processes in U-space. The concept is based on a series of fundamental principles, which sees the operators as the final decision-makers, reduces constraints on drone trajectories as much as possible, and prioritizes DCB measures based on their impact on the fulfillment of the drone missions.
DACUS has characterized the urban environments from the perspective of the expected ground infrastructure, airspace design, CNS performances, and local regulatory frameworks in order to define the boundary conditions for the implementation of DCB processes in U-space. DACUS proposes three different urban environments according to the level of constraints imposed on the drone operator.
DACUS has also defined a framework of performance indicators that drives the DCB processes from the strategic up to the tactical phase. Decisions such as the most appropriate DCB measures will be taken based on the continuous quantification of such indicators.
DACUS has also developed a separation management process that fits into the previously designed DCB process, providing a description of the set of principles and assumptions that are applied to describe the separation management, as well as the responsibility roles that are going to be determined.
Finally, DACUS has defined the requirements of key U-space services which are part of the DCB process. In particular, the Dynamic Capacity Management service, which is at the core of the overall process, relies on two models that were developed by DACUS: A collision risk model to evaluate the airspace capacity limits due to the probability of fatal injuries; and a societal impact model to evaluate the airspace capacity limits due to the noise emissions and visual impact over the population distribution during the day of operations. DACUS has developed early prototypes of those services and support models. Those prototypes were used in 4 different experiments to analyze a series of research challenges of the U-space DCB process. These questions revolve around the definition of applicable DCB measures for drones, the quantification of the required level of certainty to take decisions, the use of contingency plans within the DCB process, the definition of collision risk and societal impact assessments as a way of determining the airspace capacity limits as well as fairness and equity within the process. Conclusions of the most relevant research challenges are provided by DACUS, as well as a summary of unanswered questions which would need to be addressed in future work.
DACUS has characterized the urban environments from the perspective of the expected ground infrastructure, airspace design, CNS performances, and local regulatory frameworks in order to define the boundary conditions for the implementation of DCB processes in U-space. DACUS proposes three different urban environments according to the level of constraints imposed on the drone operator.
DACUS has also defined a framework of performance indicators that drives the DCB processes from the strategic up to the tactical phase. Decisions such as the most appropriate DCB measures will be taken based on the continuous quantification of such indicators.
DACUS has also developed a separation management process that fits into the previously designed DCB process, providing a description of the set of principles and assumptions that are applied to describe the separation management, as well as the responsibility roles that are going to be determined.
Finally, DACUS has defined the requirements of key U-space services which are part of the DCB process. In particular, the Dynamic Capacity Management service, which is at the core of the overall process, relies on two models that were developed by DACUS: A collision risk model to evaluate the airspace capacity limits due to the probability of fatal injuries; and a societal impact model to evaluate the airspace capacity limits due to the noise emissions and visual impact over the population distribution during the day of operations. DACUS has developed early prototypes of those services and support models. Those prototypes were used in 4 different experiments to analyze a series of research challenges of the U-space DCB process. These questions revolve around the definition of applicable DCB measures for drones, the quantification of the required level of certainty to take decisions, the use of contingency plans within the DCB process, the definition of collision risk and societal impact assessments as a way of determining the airspace capacity limits as well as fairness and equity within the process. Conclusions of the most relevant research challenges are provided by DACUS, as well as a summary of unanswered questions which would need to be addressed in future work.
DACUS progresses beyond the state of the art in the definition of a consistent DCB process for U-space, identifying the interactions and flow of information between the involved U-space services.
DACUS designs measures applicable to drone operations to balance demand and capacity. The measures to be implemented go beyond the existing DCB measures in manned aviation such as delaying or re-routing.
Many studies are concerned with dense or high-density drone operations but no consensus was reached on the metrics that can be used to determine when the airborne traffic is dense. DACUS defines a set of indicators that ultimately represents the probability that flights lose safe separation. This analysis is also extended to indicators that address the social impact of the drone operations to guarantee for instance that noise or visual impact is below the acceptable levels of the citizens.
Existing collision risk models consider the trajectories of aircraft and the airspace structure, but the impact of navigation and surveillance systems performance are not usually taken into account. DACUS goes beyond that consideration and analyzes the impact on the risk of collision of CNS systems performances, as well as the requirements to provide separations and avoid tactical conflicts.
DACUS researches micro-weather models applicable to urban environments, and how weather information is needed to support UAS operations with emphasis on the impact of weather forecast uncertainty in the drone Operation Plan uncertainty.
The previously defined collision risk model allows identifying the separation requirements as a function of traffic density and complexity. In urban environments, the ground risk makes it necessary to reduce the collision risk, determining the type of separation to be provided and the CNS performance requirements to keep the collision risk value below the identified threshold.
DACUS activities have contributed to ensuring safe and efficient operations, increasing the number of simultaneous operations, supporting social acceptance of urban air mobility, guiding the future development of urban airspaces and infrastructure, and ensuring the alignment with the Drone Operators’ requirements in urban environments.
DACUS designs measures applicable to drone operations to balance demand and capacity. The measures to be implemented go beyond the existing DCB measures in manned aviation such as delaying or re-routing.
Many studies are concerned with dense or high-density drone operations but no consensus was reached on the metrics that can be used to determine when the airborne traffic is dense. DACUS defines a set of indicators that ultimately represents the probability that flights lose safe separation. This analysis is also extended to indicators that address the social impact of the drone operations to guarantee for instance that noise or visual impact is below the acceptable levels of the citizens.
Existing collision risk models consider the trajectories of aircraft and the airspace structure, but the impact of navigation and surveillance systems performance are not usually taken into account. DACUS goes beyond that consideration and analyzes the impact on the risk of collision of CNS systems performances, as well as the requirements to provide separations and avoid tactical conflicts.
DACUS researches micro-weather models applicable to urban environments, and how weather information is needed to support UAS operations with emphasis on the impact of weather forecast uncertainty in the drone Operation Plan uncertainty.
The previously defined collision risk model allows identifying the separation requirements as a function of traffic density and complexity. In urban environments, the ground risk makes it necessary to reduce the collision risk, determining the type of separation to be provided and the CNS performance requirements to keep the collision risk value below the identified threshold.
DACUS activities have contributed to ensuring safe and efficient operations, increasing the number of simultaneous operations, supporting social acceptance of urban air mobility, guiding the future development of urban airspaces and infrastructure, and ensuring the alignment with the Drone Operators’ requirements in urban environments.