Deep-space missions carry an ever larger set of different and complementary onboard payloads. Each payload generates data, and synthesizing it for optimized downlinking is one way to reduce the ratio of mission costs to science return. This is the main role of the Mars-Express Scheduling Architecture (Mexar2), an AI-based tool in daily use on the Mars-Express mission since February 2005. Mexar2 supports space mission planners continuously as they plan data downlinks from the spacecraft to Earth. The tool lets planners work at a higher abstraction level while it performs low-level, often-repetitive tasks. It also helps them produce a plan rapidly, explore alternative solutions, and choose the most robust plan for execution. Additionally, planners can analyze any problems over multiple days and identify payload overcommitments that cause resource bottlenecks and increase the risk of data losses. Mexar2 has significantly increased the data return over the whole Mars-Express mission duration. Its effectively become a work companion for mission planners at the European Space Agencys European Space Operations Center (ESOC) in Darmstadt, Germany.
MEXAR2: AI Solves Mission Planner Problems
IEEE Computer Society,, Los Alamitos, CA , Stati Uniti d'America
IEEE intelligent systems 22 (2007): 12–19. doi:10.1109/MIS.2007.75
info:cnr-pdr/source/autori:Cesta, A.; Cortellessa, G.; Denis, M.; Donati, A.; Fratini, S.; Oddi, A.; Policella, N.; Rabenau, E.; Schulster, J./titolo:MEXAR2: AI Solves Mission Planner Problems/doi:10.1109/MIS.2007.75/rivista:IEEE intelligent systems/anno: