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Ant-Based Mission Planning for Constellations: A Generic Framework Applied to EO and Data Relay Missions

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Space Operations: Inspiring Humankind's Future

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Abstract

The earth observation market is growing rapidly, along with the missions’ complexity. Therefore, automated mission planning systems are being designed, allowing for operators to simply specify their intentions on a high level. In this paper, we propose an automated mission planning system based on the ants' foraging mechanism and apply it to two different mission planning problems, from an earth imaging and a data relay mission, investigating the system's ability to be generalized. We compare the planning process for the two problems and generalize on the type of planning problems the system can address.

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Abbreviations

ACO:

Ant colony optimization

AoI:

Area of interest

EO:

Earth observation

GEO:

Geosynchronous orbit

GS:

Ground station

LEO:

Low earth orbit

MPS:

Mission planning system

P&S:

Planning and scheduling

References

  1. Chien, S. A., Johnston, M., Frank, J., Giuliano, M., Kavelaars, A., Lenzen, C., & Policella, N. (2012). A generalized timeline representation, services, and interface for automating space mission operations. In SpaceOps 2012. Stockholm: [s.n.].

    Google Scholar 

  2. Donati, A., Policella, N., Cesta, A., Fratini, S., Oddi, A., Cortellessa, G., et al. (2008). Science operations pre-planning & optimization using AI constraint-resolution—the APSI case study 1. In SpaceOps 2008.

    Google Scholar 

  3. Dorigo, M., & Stutzle, T. (2004). Ant colony optimization. [s.l.]: MIT Press.

    Google Scholar 

  4. Dvorak, D. D., Ingham, M. D., Morris, J. R., & Gersh, J. (2009). Goal-based operations: An overview. Journal of Aerospace Computing, Information, and Communication, 6(3), 123–141.

    Article  Google Scholar 

  5. Gramling, J., & Chrissotimos, N. (2008). Three generations of NASA’s tracking and data relay satellite system. In SpaceOps 2008.

    Google Scholar 

  6. Iacopino, C., & Palmer, P. (2012). The dynamics of ant colony optimization algorithms applied to binary chains. Swarm Intelligence, pp. 343–377.

    Google Scholar 

  7. Iacopino, C., Palmer, P., Brewer, A., Policella, N., & Donati, A. (2013). EO constellation MPS based on ant colony optimization algorithms. In 6th International Conference on Recent Advances in Space Technologies—RAST 2013.

    Google Scholar 

  8. Johnston, M. D., & Giuliano, M. (2011). Multi-objective scheduling for the cluster II constellation. In 6th International Workshop on Planning and Scheduling in Space.

    Google Scholar 

  9. Maurer, E., Mrowka, F., Braun, A., Geyer, M., Lenzen, C., Wasser, Y., & Wickler, M. (2010). TerraSAR-X mission planning system: Automated command generation for spacecraft operations. IEEE Transactions on Geoscience and Remote Sensing, pp. 642–648.

    Google Scholar 

  10. Ntagiou, E., Armellin, R., Iacopino, C., Policella, N., & Donati, A. (2018). Ant-based mission planning: Two examples. In SpaceOps 2018—The 15th International conference on Space Operations. Marseille: [s.n.].

    Google Scholar 

  11. Ntagiou, E., Armellin, R., Iacopino, C., Policella, N., & Donati, A. (2017). Ant colony optimization applied to the planning of a data relay space mission. In International Workshop on Planning and Scheduling in Space.

    Google Scholar 

  12. Ntagiou, E., Armellin, R., Iacopino, C., Policella, N., & Donati, A. (2017). Coverage planning for earth observation constellations. In International Conference on Automated Planning and Scheduling—Scheduling and Planning Applications woRKshop. Pittsburgh: [s.n.].

    Google Scholar 

  13. Policella, N., Oliveira, H., & Siili, T. (2009). Managing SOHO’s keyhole periods: Problem definition and solving model. In Proceedings of the International Workshop on Planning and Scheduling in Space.

    Google Scholar 

  14. Smith, B., Engelhardt, B., & Mutz, D. (2002). The RADARSAT-MAMM automated mission planner. AI Magazine, 25–36.

    Google Scholar 

  15. Strimel, G. P., & Veloso, M. M. (2014). Coverage planning with finite resources. In International Conference on Intelligent Robots and Systems (IROS).

    Google Scholar 

  16. Tejo, J., Pereda, M., Veiga, I., Chamoun, J., Garcia, G., & Beech, T. (2006). flexplan: An operational mission planning & scheduling COTS used internationally. In 5th International Workshop on Planning and Scheduling in Space. Baltimore: [s.n.].

    Google Scholar 

  17. Uhlig, T., Sellmaier, F., & Schmidhuber, M. (2015). Spacecraft operations [s.l.]: Springer.

    Google Scholar 

  18. Wallrapp, F., Ballweg, R., & Gataullin, Y. (2011). The European Data Relay System (EDRS): Operational challenges. In 62nd International Astronautical Congress, Cape Town: [s.n.].

    Google Scholar 

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Acknowledgements

This work is co-funded by the Surrey Space Centre (SSC) of the University of Surrey; the Surrey Satellite Technology Ltd (SSTL); and the Operations Centre of the European Space Agency (ESA/ESOC).

Author information

Authors and Affiliations

  1. University of Surrey, Guildford, UK

    Evridiki V. Ntagiou & Roberto Armellin

  2. Surrey Satellite Technology, Guildford, UK

    Claudio Iacopino

  3. ESOC, European Space Agency, Darmstadt, Germany

    Nicola Policella & Alessandro Donati

Authors
  1. Evridiki V. Ntagiou
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  2. Roberto Armellin
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  3. Claudio Iacopino
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  4. Nicola Policella
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  5. Alessandro Donati
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Corresponding author

Correspondence to Evridiki V. Ntagiou .

Editor information

Editors and Affiliations

  1. Direction du Numérique, de l'exploitation et des Opérations, Centre National D’Etudes Spatiales—CNES, Toulouse, France

    Helene Pasquier

  2. Payload and Mission Operations, NASA Marshall Space Flight Center, Huntsville, AL, USA

    Craig A. Cruzen

  3. German Space Operations Center, DLR—German Aerospace Center, Wessling, Bayern, Germany

    Michael Schmidhuber

  4. NASA/Jet Propulsion Laboratory/California Institute of Technology, Pasadena, CA, USA

    Young H. Lee

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Ntagiou, E.V., Armellin, R., Iacopino, C., Policella, N., Donati, A. (2019). Ant-Based Mission Planning for Constellations: A Generic Framework Applied to EO and Data Relay Missions. In: Pasquier, H., Cruzen, C., Schmidhuber, M., Lee, Y. (eds) Space Operations: Inspiring Humankind's Future. Springer, Cham. https://doi.org/10.1007/978-3-030-11536-4_28

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  • DOI: https://doi.org/10.1007/978-3-030-11536-4_28

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-11535-7

  • Online ISBN: 978-3-030-11536-4

  • eBook Packages: EngineeringEngineering (R0)

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