I have stumbled upon two news releases from the European Space Agency (ESA) about the use of AI techniques to help to improve the performance and quality acquisition of science data from the Mars Express Mission. The first news release is an interview with Dr Ari Kristinn Jónsson, Dean of the School of Computer Science at Reykjavik University, Iceland, and former research scientist at NASA’s Ames Research Center, where he led various projects both in AI development and in AI applications for space missions.
29 April 2008
Dr Ari Kristinn Jónsson says that artificial intelligence (AI) can open up new opportunities for the long-term exploration of the solar system, supporting missions that require minimal oversight from human controllers on Earth.
The second news release deals specifically with the problem in Mars Express that has been resolved with the help of MEXAR2 (‘Mars Express AI Tool’), developed by AI researchers at Italy’s Institute for Cognitive Science and Technology (ISTC-CNR) led by Dr Amedeo Cesta and mission planners and computer scientists at the European Space Operations Centre (ESOC) in Darmstadt, Germany.
29 April 2008
Artificial intelligence (AI) being used at the European Space Operations Centre is giving a powerful boost to ESA’s Mars Express as it searches for signs of past or present life on the Red Planet.
Traditionally, data downloading was managed using human-operated scheduling software to generate command sequences sent to Mars Express, telling it when to dump specific data packets. “This is tedious, time-consuming and never really eliminated the occasional loss – forever – of valuable science data,” says Alessandro Donati, Head of the Advanced Mission Concepts and Technologies Office at ESA’s Space Operations Centre (ESOC), Darmstadt, Germany.
But since 2005, AI researchers at Italy’s Institute for Cognitive Science and Technology (ISTC-CNR) led by Dr Amedeo Cesta and mission planners and computer scientists at ESOC have been developing a solution to the complex Mars Express scheduling problem by applying artificial intelligence (AI) techniques to the problem. These are similar to those used to solve scheduling and optimisation problems faced by airlines, shipping companies and large construction projects.