Research > Projects > CALIPSO

This project deals with the scheduling of activities of space mission experiments, specifically of the observation from orbit of ground targets. To this end, it aims at integrating two well-known disciplines of AI, operation research (OR) and machine learning (ML), in order to build an effective, flexible and dynamic scheduler. Techniques from these two research areas will be adapted to deal with the characteristics and constraints of spacecrafts and instruments onboard. The interconnection between these research areas and their application to planetary remote sensing represents the strength and originality of the project. ML will be devoted to the study of data collected by on board instruments. At the same time, OR algorithms will address the problem of scheduling of observations in the complex context of space exploration, taking into account the outcome of ML algorithms.

Planned activities:
• develop an effective and flexible optimization algorithm to support the planning of space mission operations;
• exploit the potential of ML to both guide the optimization and to retrieve useful information;
• include the uncertainty and dynamicity of space missions in the optimization approach;
• develop a user-friendly tool to plan activities of space missions.