“By using AI to enhance autonomous monitoring, detection, processing and data transfer capabilities on spacecraft, we are shaping new mission paradigms in space exploration.”
Thales Alenia Space and the start-up AIKOS.r.l. signed a Memorandum of Understanding (MoU) to jointly develop advanced software for space systems.
Their partnership will primarily study the use of artificial intelligence (AI) and machine learning technologies to enhance the autonomous operation of satellites and space infrastructures, working in conjunction with the companies’ research and engineering teams. With the data generated by space systems increasing exponentially and space missions becoming more and more complex, advanced software solutions that can maximize the chances of mission success have become fundamental. AI technologies can address these needs, providing scalability and robustness in the harsh space environment. Thales Alenia Space and AIKO will capitalize on their decades of experience in the software and space sectors to support the next generation of space missions.
“Thales Alenia Space acts as a catalyst in the space ecosystem by tackling the challenges of an emerging economy in which space is becoming one of the foundations,” said Massimo Claudio Comparini, Senior Executive Vice President Observation, Exploration and Navigation at Thales Alenia Space. “We have confirmed our pivotal position in the space industry and we are very proud of our partnerships, such as this latest one with AIKO. By using AI to enhance autonomous monitoring, detection, processing and data transfer capabilities on spacecraft, we are shaping new mission paradigms in space exploration.”
“This MoU is a major step forward for our company,” added Lorenzo Feruglio, CEO of AIKO, “since it signals the application of AI to more complex, ambitious missions. Thales Alenia Space has a proven track record in these missions and we are delighted to be working with them. Furthermore, our partnership paves the way for missions that will address end-users’ evolving needs in terms of data volumes and information timeliness.”
