Deliverables
Description of the results of the validation experiments and analysis of suitability of the adapted prototype algorithms in view of the generation of the agreed KPIs Results from the validation process will form inputs to the final integrated and optimized drone DCB process WP1 as well as for the update of the architecture and algorithms of WP2 services
Separation management process definitionDefinition of procedural and tactical separation schemes and description of appropriate measures and rule based regulation schemes needed to adapt special traffic separation areas.
Refined CNS requirementsRefinement of the preliminary set of CNS requirements in support of the separation minima criteria and the separation management process based on the results of FTS performed in WP4
Structures and Rules in Capacity Constrained (urban) EnvironmentsDescription of an environment adapted set of uniform, risk based, mission aware airspace structures and rules. All rules, structures and measures need to be compliant with the boundary conditions found in complex aeronautical environments, such as urban areas with a dense population or high traffic volumes. The deliverable also includes the identification of theses potential limiting conditions.
Final optimised drone DCBReview and update of the process defined in T11 in light of the FTS performed in WP4 and the developments made in WP2 related to services algorithms contributing to the drone DCB performance Consolidation of results in the form of proposal of figures related to acceptable residual risk as driver of dynamic capacity management
Final Project Results ReportThis deliverable provides the DACUS findings which will include the description of final DACUS DCB concept a summary of supporting models and algorithms and the results with evidence of benefits and operational feasibility
Drone DCB concept and processes, including influence factorsDefinition of initial drone DCB concept.
Scenarios for validation experimentsA set of operational scenarios which will allow the team to perform a series of validation experiments aimed at testing the suitability and performance of the various prototype algorithms under nominal and sub-nominal operating conditions, as well as to support the analysis of separation intelligence balance and refinement of CNS requirements linked to separation minima criteria.
Dynamic separation minima criteriaAs a result of the risk model of D32 a set of dynamic separation minima criteria will be defined
DACUS Performance FrameworkA performance framework for the DACUS concept that reflects the main policies and goals in a way that can be quantitatively measured Key performance indicators will be identified and lead to a specific metric for evaluating the DACUS concept
Innovative data driven decision making algorithms (including but not limited to Machine Learning algorithms) and the associated architecture. Development of a framework that secures and optimizes the flow demand for urban drone traffic in balance with the capacity of U-Space. The base for the dynamic capacity management process are the influence factors defined in WP1 and the outcomes defined for the dynamic input models in WP3. First delivery (T12) includes a software framework ready for testing in FTS; final delivery (T22) addresses algorithm updates with FTS results.
Drone trajectory management framework prototypesDevelopment and evolution of services for mission planning management, flight planning management and weather in line with drone DCB concept, structures and rules in capacity constrained environments and separation management process defined. First delivery (T12) includes services ready for testing in FTS; final delivery (T22) addresses algorithms updates with FTS results.
Models and enabling technologies in support of DCB descriptionMathematical model to identify the potential collision risk (for every pair of drones, and global one) considering the traffic mix and drones features, the real performances provided by the CNS and Computing systems, the population density, etc. It addresses risk and societal impact. First delivery (T7) includes outputs format and content defined; second delivery: (T10) completes conceptual models and algorithms.
AI Demand Prediction ModelModel for forecast the real demand considering operational models and characterization expressed in the WP2 for each type of drone introducing a variable coefficient impacted by micro-weather event. First delivery (T7) includes outputs format and content defined; second delivery (T10) addresses the conceptual model and algorithm.
Prototype of services to support large number of simultaneous operationsDevelopment and evolution of services for strategic and adaptive tactical de-confliction, considering collision risk models based on the navigation, communications and tracking performances as well as drone traffic density. First delivery (T12) includes services ready for testing in FTS; final delivery (T22) addresses algorithms updates with FTS results.
Publications
Author(s):
Pablo Sánchez Escalonilla, Dominik Janisch, ATM R&D Reference Center, CRIDA.
Chris Forster, Altitude Angel Reading.
Michael Büddefeld, Hugo Eduardo Teomitzi, Technische Universität Darmstadt.
Published in:
SESAR Innovation Days 2020, Issue yearly, 2020
Publisher:
SESAR
Author(s):
Michael Bueddefeld, Ian Crook, Hugo Eduardo Teomitzi, Jan Kleikemper, Thibaut Picot, Pablo Sánchez Escalonilla, Yannick Seprey
Published in:
Journal of Physics: Conference Series, Issue Upon acceptance, 2022
Publisher:
IOP SCIENCE
Author(s):
Dominik Janisch D, Pablo Sánchez Escalonilla,Víctor Gordo, Marta Jiménez
Published in:
SESAR Innovation Days 2021, Issue yearly, 2021
Publisher:
SESAR
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