Constraints

, Volume 20, Issue 1, pp 77–99 | Cite as

Scheduling scientific experiments for comet exploration

  • Gilles Simonin
  • Christian Artigues
  • Emmanuel Hebrard
  • Pierre Lopez
Application

Abstract

The Rosetta/Philae mission was launched in 2004 by the European Space Agency (ESA). It is scheduled to reach the comet 67P/Churyumov-Gerasimenko in November 2014 after traveling more than six billion kilometers. The Philae module will then be separated from the orbiter (Rosetta) to attempt the first ever landing on the surface of a comet. If it succeeds, it will engage a sequence of scientific exploratory experiments on the comet. In this paper, we describe a constraint programming model for scheduling the different experiments of the mission. A feasible plan must satisfy a number of constraints induced by energetic resources, precedence relations on tasks, and incompatibility between instruments. Moreover, a very important aspect is related to the transfer (to the orbiter then to the Earth) of all the data produced by the instruments. The capacity of inboard memories and the limitation of transfers within visibility windows between lander and orbiter, make the transfer policy implemented on the lander CPU prone to data loss. We introduce a global constraint to handle data transfers. The purpose of this constraint is to ensure that data-producing tasks are scheduled in such a way that no data is lost. Thanks to this constraint and to the filtering rules we propose, mission control is now able to compute feasible plans in a few seconds for scenarios where minutes were previously often required. Moreover, in many cases, data transfers are now much more accurately simulated, thus increasing the reliability of the plans.

Keywords

Global constraint Scheduling Data transfer Energy and memory constraints Space experiments 

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References

  1. 1.
    Aggoun, A., & Beldiceanu, N. (1993). Extending CHIP in order to solve complex scheduling and placement problems. Mathematical and Computer Modelling, 17(7), 57–73.CrossRefMathSciNetGoogle Scholar
  2. 2.
    Philippe, B., Le Pape, C., Nuijten, W. (2001). Constraint-Based Scheduling: Springer.Google Scholar
  3. 3.
    Beldiceanu, N., & Carlsson, M. (2001). Sweep as a generic pruning technique applied to the non-overlapping rectangles constraint, In: Seventh International Conference on Principles and Practice of Constraint Programming (CP 2001), LNCS 2239, pp. 377–391. Springer.Google Scholar
  4. 4.
    Cesta, A., Cortellessa, G., Denis, M., Donati, A., Fratini, S., Oddi, A., Policella, N., Rabenau, E., Schulster, J. (2007). Mexar2: AI solves mission planner problems. IEEE Intelligent Systems, 22(4), 12–19.CrossRefGoogle Scholar
  5. 5.
    Philippe, L. (2003). Algorithms for propagating resource constraints in AI planning and scheduling: existing approaches and new results. Artificial Intelligence, 143(2), 151–188.CrossRefMATHMathSciNetGoogle Scholar
  6. 6.
    Mancel, C., & Lopez, P. (2003). Complex optimization problems in space systems, In: 13th International Conference on Automated Planning & Scheduling (ICAPS’03), Doctoral Consortium.Google Scholar
  7. 7.
    Oddi, A., & Policella, N. (2007). Improving robustness of spacecraft downlink schedules. IEEE Transactions on Systems, Man, and Cybernetics, Part C, 37(5), 887–896.CrossRefGoogle Scholar
  8. 8.
    Righini, G., & Tresoldi, E. (2010). A mathematical programming solution to the Mars Express memory dumping problem. IEEE Transactions on Systems, Man, and Cybernetics, Part C, 40(3), 268–277.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Gilles Simonin
    • 1
    • 2
  • Christian Artigues
    • 1
    • 2
  • Emmanuel Hebrard
    • 1
    • 2
  • Pierre Lopez
    • 1
    • 2
  1. 1.CNRS, LAASToulouseFrance
  2. 2.Univ de ToulouseToulouseFrance

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