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 organize 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 organization 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.
