Artificial intelligence for the EChO long-term mission planning tool

Álvaro García-Piquer; Ignasi Ribas; Josep Colomé

doi:10.1117/12.2056446

7 August 2014 Artificial intelligence for the EChO long-term mission planning tool

Álvaro García-Piquer, Ignasi Ribas, Josep Colomé

Author Affiliations +

Proceedings Volume 9149, Observatory Operations: Strategies, Processes, and Systems V; 91491U (2014) https://doi.org/10.1117/12.2056446
Event: SPIE Astronomical Telescopes + Instrumentation, 2014, Montréal, Quebec, Canada

Abstract

The Exoplanet Characterisation Observatory (EChO) was an ESA mission candidate competing for a launch opportunity within the M3 call. Its main aim was to carry out research on the physics and chemistry of atmospheres of transiting planets. This requires the observation of two types of events: primary and secondary eclipses. The events of each exoplanet have to be observed several times in order to obtain measurements with adequate Signal-to-Noise Ratio. Furthermore, several criteria must be considered to perform an observation, among which we can highlight the exoplanet visibility, its event duration, and the avoidance of overlapping with other tasks. It is important to emphasize that, since the communications for transferring data from ground stations to the spacecraft are restricted, it is necessary to compute a long-term plan of observations in order to provide autonomy to the observatory. Thus, a suitable mission plan will increase the efficiency of telescope operation, and this will result in a raise of the scientific return and a reduction of operational costs. Obtaining a long-term mission plan becomes unaffordable for human planners due to the complexity of computing the large amount of possible combinations for finding a near-optimal solution. In this contribution we present a long-term mission planning tool based on Genetic Algorithms, which are focused on solving optimization problems such as the planning of several tasks. Specifically, the proposed tool finds a solution that highly optimizes the objectives defined, which are based on the maximization of the time spent on scientific observations and the scientific return (e.g., the coverage of the mission survey). The results obtained on the large experimental set up support that the proposed scheduler technology is robust and can function in a variety of scenarios, offering a competitive performance which does not depend on the collection of objects to be observed. Finally, it is noteworthy that the conducted experiments allow us to size some aspects of the mission with the aim of guaranteeing its feasibility.

Citation Download Citation

Álvaro García-Piquer, Ignasi Ribas, and Josep Colomé "Artificial intelligence for the EChO long-term mission planning tool", Proc. SPIE 9149, Observatory Operations: Strategies, Processes, and Systems V, 91491U (7 August 2014); https://doi.org/10.1117/12.2056446

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