TY - GEN
T1 - Artificial intelligence for the EChO long-term mission planning tool
AU - García-Piquer, Álvaro
AU - Ribas, Ignasi
AU - Colomé, Josep
N1 - Publisher Copyright:
© 2014 SPIE.
PY - 2014
Y1 - 2014
N2 - 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.
AB - 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.
KW - Constraint-Based Reasoning
KW - Genetic Algorithms
KW - Observatory Operations
KW - Planning
KW - Scheduling
KW - Space Applications
UR - http://www.scopus.com/inward/record.url?scp=84922635819&partnerID=8YFLogxK
U2 - 10.1117/12.2056446
DO - 10.1117/12.2056446
M3 - Conference contribution
AN - SCOPUS:84922635819
T3 - Proceedings of SPIE - The International Society for Optical Engineering
BT - Observatory Operations
A2 - Peck, Alison B.
A2 - Benn, Chris R.
A2 - Seaman, Robert L.
PB - SPIE
T2 - Observatory Operations: Strategies, Processes, and Systems V
Y2 - 25 June 2014 through 27 June 2014
ER -