Achievements and status: ​

The ambitious goal to fully apply Model-Based Systems Engineering (MBSE) in future projects and to establish the methodology across ESA missions, engineering domains, customer-supplier boundaries and lifecycle phases demands that the entire lifecycle is addressed. This includes - among others - procurement, operations and the transition of engineering model data from Phase 0/A through to Phase E. For instance, the utilisation of digital system engineering data or models upon handover to the operator is limited and based on documents or static database artefacts.

FITAI is a Machine Learning based technique that improves GNSS positioning engine accuracy by mapping signal strength and residuals into position errors. These inputs are fed into a neural network to learn and correct errors, offering a non-invasive solution that provides better location precision without relying on external sources like reference stations.

Convolutional Neural Networks (CNNs) are more and more being used to solve very different kind of problems on ground and are also suitable to solve Space problems, e.g. visual based tasks for rovers. Embedding a CNN into an operational on-board software (OBSW) or hardware is challenging as the CNNs are composed by hundreds of operations and they request an extensive amount of memory that need to be used efficiently.

The ASIL2ECSS ESA-funded activity aims to reduce the space qualification cost by reusing hardware and software developed for the automotive domain, which complies with the ISO 26262 functional safety standard. In order to do so, ASIL2ECSS created a mapping between the various ECSS standards and ISO 26262 and related automotive standards in a structured way.

The general goal of this project is to operationalize and deepen the findings and conclusions of the initial project “Exploring AI capabilities to support the evaluation process of ideas submitted in the OSIP platform.” which demonstrated that AI techniques and software (COGITO Discover) could be used positively to enhance and ease the evaluation of ideas, by providing some new indicators such as idea novelty (scoring 0-100) based on the notion of similarity between ideas and innovation projects.

The space industry and space agencies have been aware for quite some time of the interest and added value of model-based system engineering (MBSE) in streamlining the design, development, deployment and verification of space systems. There have been many initiatives during the past decades aiming at defining models and tools able to support the various needs of the system engineering process. MBSE is increasingly being used in operational projects and the next step is to use MBSE extensively throughout the system life cycle, across disciplines and throughout the supply chain.