Summary
With an increasing number and diversity of potential drone operations, managing the airspace to accommodate these drones will become an increasingly sophisticated task, especially in densely populated urban areas encompassing restricted zones with dynamic environmental and operational influences. Due to the associated higher probability of conflicts, and ultimately collisions, such areas require management of dedicated structured airspace, operations, and services to help mitigate these potential hazards.
Several projects are currently working on defining ConOps for U-space services. Corus and Corus-XUAM have defined a possible capabilities model, such as airspace organization and services. However, a holistic framework is necessary to create an effective and efficient flow of information between the various capabilities in order to systematically organise the airspace usage. Such an automated Air Traffic Management System will be essential for the introduction drone operations at scale.
AI4HyDrop evaluates the various stakeholder needs, delivering validated concepts, defining a methodology for an airspace structure organisation and associated U-space services. The framework considers the information from other services such as meteorological and separation provision, which can then be used for flight planning approval process, prioritization. In addition, essential elements such as surveillance and contingency planning can be addressed. The framework incorporates various AI based tools and associated information flows necessary to addresses the complexity, safety and scalability required for implementing such U-space services.
The proposed framework represents a digital step change in ATM, using AI as a means to overcome many critical barriers foreseen in the introduction of automated U-space services. The findings could later be expanded to support general airspace management.
Several projects are currently working on defining ConOps for U-space services. Corus and Corus-XUAM have defined a possible capabilities model, such as airspace organization and services. However, a holistic framework is necessary to create an effective and efficient flow of information between the various capabilities in order to systematically organise the airspace usage. Such an automated Air Traffic Management System will be essential for the introduction drone operations at scale.
AI4HyDrop evaluates the various stakeholder needs, delivering validated concepts, defining a methodology for an airspace structure organisation and associated U-space services. The framework considers the information from other services such as meteorological and separation provision, which can then be used for flight planning approval process, prioritization. In addition, essential elements such as surveillance and contingency planning can be addressed. The framework incorporates various AI based tools and associated information flows necessary to addresses the complexity, safety and scalability required for implementing such U-space services.
The proposed framework represents a digital step change in ATM, using AI as a means to overcome many critical barriers foreseen in the introduction of automated U-space services. The findings could later be expanded to support general airspace management.
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| Web resources: | https://cordis.europa.eu/project/id/101114805 |
| Start date: | 01-09-2023 |
| End date: | 28-02-2026 |
| Total budget - Public funding: | 1 993 112,50 Euro - 1 993 112,00 Euro |
Cordis data
Original description
With an increasing number and diversity of potential drone operations, managing the airspace to accommodate these drones will become an increasingly sophisticated task, especially in densely populated urban areas encompassing restricted zones with dynamic environmental and operational influences. Due to the associated higher probability of conflicts, and ultimately collisions, such areas require management of dedicated structured airspace, operations, and services to help mitigate these potential hazards.Several projects are currently working on defining ConOps for U-space services. Corus and Corus-XUAM have defined a possible capabilities model, such as airspace organization and services. However, a holistic framework is necessary to create an effective and efficient flow of information between the various capabilities in order to systematically organise the airspace usage. Such an automated Air Traffic Management System will be essential for the introduction drone operations at scale.
AI4HyDrop evaluates the various stakeholder needs, delivering validated concepts, defining a methodology for an airspace structure organisation and associated U-space services. The framework considers the information from other services such as meteorological and separation provision, which can then be used for flight planning approval process, prioritization. In addition, essential elements such as surveillance and contingency planning can be addressed. The framework incorporates various AI based tools and associated information flows necessary to addresses the complexity, safety and scalability required for implementing such U-space services.
The proposed framework represents a digital step change in ATM, using AI as a means to overcome many critical barriers foreseen in the introduction of automated U-space services. The findings could later be expanded to support general airspace management.
Status
SIGNEDCall topic
HORIZON-SESAR-2022-DES-ER-01-WA2-4Update Date
31-07-2023
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Organisations
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| UNIVERSITETET I SOROST-NORGE (USN) (Coördinator)
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| DEUTSCHES ZENTRUM FUR LUFT - UND RAUMFAHRT EV
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| DEVLET HAVA MEYDANLARI ISLETMESI GENEL MUDURLUGU - DHMI
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| DUZCE BELEDIYESI
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| Istanbul Technical University
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| KATARA
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| SINTEF AS (SINTEF)
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| SOPRA STERIA GROUP
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| UNIVERSIDAD EUROPEA DE MADRID, SAU
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| VYZKUMNY A ZKUSEBNI LETECKY USTAV AS