The MMT project officially kicked-off on March 16, 2018 in the presence of Florence Parly, the French Minister of the Armed Forces, and Joël Barre, head of the French Defense Procurement Agency (DGA), with project management by Dassault Aviation and Thales. Nineteen projects had already been launched. Notified at the end of 2018, these projects are already beginning to show promising prospects.
MMT expects that a quarter of these studies will be entrusted to laboratories, innovative SMEs and mid-tier companies, and start-ups specializing in AI, robotics and new MMIs. The objective is to create an industrial ecosystem conducive to detecting innovations, assessing them, seeing them mature and, in the long run, have them used in the development of future fighter aircraft, starting from the Rafale update by 2030 in particular, followed by the Future Combat Air System (FCAS).
In April 2019, the new topics to be studied were selected by the armed forces, the DGA, Dassault Aviation and Thales. They are categorized into the six main MMT themes:
--Virtual assistant & smart cockpit;
--Robotic support & maintenance.
Out of 175 proposals received from manufacturers, demonstrating the success of the MMT operation, 19 projects were ultimately selected, with work lasting from 8 to 12 months. They involve 16 SMEs and mid-tier companies, 8 start-ups, as well as 4 laboratories from all over France.
ENIB: incarnation and communication principles for a cockpit virtual assistant
PROBAYES: reasoning in a probabilistic tactical environment
SYNAPSE DEV: building and enriching business-specific ontology from textual resources and use in an information search scenario
ESTIA: monitoring man-machine performance through the analysis of trust and cooperation
NAOX: in-ear monitoring of cognitive processes and vigilance
ELLCIE: crew monitoring by infrared video-oculography in an environment with military constraints
PHYSIP: development of innovative conductive material for EEG recording
ARTELYS: optimization of mission patterns
ELTER: approximation of complex functions through learning
NUMALIS: explaining choices made by a neural network
EXWEX: merging of multi-source weather data for prediction in mission preparation and in operation onboard a fighter aircraft
EARTHCUBE: contribution of multi-spectral learning methods to satellite imagery
EARTHCUBE: contribution of learning methods from different algorithms to satellite imagery
OKTAL: mass production of coherent optical and radar data by simulation
IRT Saint-Exupéry: augmented simulation of sensors by neural networks
NUMALIS: validation of neural networks for avionics optronics analysis
ACSYSTEM: fault recognition through learning
ESI GROUP: digital twin
LATESYS: reconstitution of spare parts for additive manufacturing