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JACIII Vol.15 No.8 pp. 1159-1166
doi: 10.20965/jaciii.2011.p1159
(2011)

Paper:

Benefits of Using Innovative Tools for Diagnostics and Planning in ESA Mission Operations

Alessandro Donati, Jose Antonio Martinez-Heras,
and Nicola Policella

Advanced Mission Concepts and Technologies Office, ESA-ESOC, European Space Agency, Robert-Bosch-Str. 5, D-64293 Darmstadt, Germany

Received:
May 15, 2011
Accepted:
August 1, 2011
Published:
October 20, 2011
Keywords:
monitoring and diagnostics, planning and scheduling, artificial intelligence, data mining, space operation
Abstract

Future European Space Agency (ESA) space missions are demanding and driving new operations concepts for increased on-board autonomy, for flexible and robust planning and scheduling services, and for ground capabilities to agglomerate and process a huge amount of downlinked data (e.g., tens of thousands of telemetry parameters) to extract high-level information and knowledge. Mission control will have to cope with maintaining and programming challenging missions such as interplanetary probes, complex scientific missions, and a constellation of earth-observation missions. The process of innovation in these areas is already progressing at the European Space Operations Centre (ESOC) of the ESA, and this paper highlights specific achievements and trends in the area of spacecraft diagnosis and mission planning and scheduling by making use of a variety of technologies and techniques. The discussion then focuses on the tools’ operational impact and on the expected trends in the future.

Cite this article as:
Alessandro Donati, Jose Antonio Martinez-Heras, and
and Nicola Policella, “Benefits of Using Innovative Tools for Diagnostics and Planning in ESA Mission Operations,” J. Adv. Comput. Intell. Intell. Inform., Vol.15, No.8, pp. 1159-1166, 2011.
Data files:
References
  1. [1] A. Donati and A. Baumgartner, “Happy Chickens from fresh eggs – innovative technologies for mission operations,” ESA Bulletin, Nov. 2008.
  2. [2] http://www.esa.int/SPECIALS/Operations/SEM30PYRA0G_0.html
  3. [3] R. Clivio, A. McGarry, and A. Donati, “Phaecian: Application of Fuzzy Logic to the Ulysses Nutation,” DASIA 2001, Nice, 2001.
  4. [4] A. Donati, J. A. Martinez-Heras, P. Nunes, and R. Torrao, “Virtual Reality for Monitoring Spacecraft Thermal Subsystem: Concept and Prototype,” Poster at SpaceOps 2004, Montreal, Canada, 2004.
  5. [5] J. A. Martinez-Heras, A. Baumgartner, and A. Donati, “MUST: Mission Utility and Support Tools,” Proc. DASIA 2005 Conf., Edinburgh, UK., 2005.
  6. [6] A. Baumgartner, J. A.Martinez-Heras, A. Donati, andM. Quintana-Claramunt, “MUST – A Platform for Introducing Innovative Technologies in Operations,” iSAIRAS 2005, Munich, Germany, 2005.
  7. [7] A. Pereira et al., “Fuzzy Expert System for Gyroscope Fault Detection,” ESM, 2002.
  8. [8] J. A. Martinez-Heras, K. Yeung, A. Donati, B. Sousa, and N. Keil, “DrMUST: Automating the Anomaly Investigation First-Cut,” In the IJCAI-09 Workshop on Artificial Intelligence in Space, Pasadena, California, USA, July 17-18, 2009.
  9. [9] F. Itakura, “Minimum prediction residual principle applied to speech recognition,” IEEE Trans Acoustics Speech Signal Process ASSP, Vol.23, pp. 52-72, 1975.
  10. [10] H. Sakoe and S. Chiba, “Dynamic programming algorithm optimization for spoken word recognition,” IEEE Trans Acoustics Speech Signal Process ASSP, Vol.26, pp. 43-49, 1978.
  11. [11] E. Keogh, “Exact indexing of dynamic time warping,” In 28th Int. Conf. on Very Large Data Bases, Hong Kong, pp. 406-417, 2002.
  12. [12] J. A. Martinez-Heras and A. Donati, “Making Sense of Housekeeping Telemetry Time Series,” In the IJCAI-11 Workshop of Artificial Intelligence in Space, Barcelona, Spain, July 17, 2011.
  13. [13] A. Donati, “ESA Strategic Building Blocks for AI P&S Infusion in Space: Current Status and Perspectives,” IWPSS 2009, Pasadena, 2009.
  14. [14] B. D. Smith, B. E. Engelhardt, and D. H. Mutz, “The RADARSATMAMM Automated Mission Planner,” AI Magazine, Vol.23, No.2, pp. 25-36, 2002.
  15. [15] L. A. Kramer, L. Barbulescu, and S. F. Smith, “Understanding Performance Tradeoffs in Algorithms for Solving Oversubscribed Scheduling,” AAAI 2007, pp. 1019-1024, 2007.
  16. [16] A. Oddi and N. Policella, “Improving Robustness of Spacecraft Downlink Schedules,” to be published in IEEE Trans. Systems Man and Cybernetics, Part C, Vol.37, No.5, 2007.
  17. [17] A. Cesta, G. Cortellessa, S. Fratini, A. Oddi, and N. Policella, “An Innovative Product for Space Mission Planning – an a posteriori evaluation,” In ICAPS-07. Proc. of the 17th Int. Conf. on Automated Planning and Scheduling, 2007.
  18. [18] E. Rabenau et al., “Using an Artificial Intelligence Tool to Perform Science Data Downlink Planning as Part of the Mission Planning Activities of Mars Express,” IWPSS 2006, Baltimore, 2006.
  19. [19] A. Cesta, G. Cortellessa, M. Denis, A. Donati, S. Fratini, A. Oddi, N. Policella, E. Rabenau, and J. Schulster, “Mexar2: AI Solves Mission Planner Problems. IEEE Intelligent Systems Vol.22, No.4, pp. 12-19, 2007.
  20. [20] E. Rabenau et al., “The RAXEM Tool on Mars Express – Uplink Planning Optimisation and Scheduling Using AI Constraint Resolution,” IWPSS 2009, Pasadena, 2009.
  21. [21] A. Cesta et al., “Developing an End-to-end Planning Application from a Timeline Representation Framework,” IAAI 2009, Pasadena, 2009.
  22. [22] A. Cesta, G. Cortellessa, S. Fratini, and A. Oddi, “MrSPOCK: a Long-term Planning Tool for Mars Express,” IWPSS 2009, Pasadena, 2009.
  23. [23] C. Pralet and G. Verfaillie, “AIMS: A Tool for Long-term Planning of the ESA INTEGRAL Mission,” IWPSS 2009 Pasadena, 2009.
  24. [24] M. Lavagna and F. Castellini, “Advanced Planning and Scheduling Initiative’s XMAS tool: AI for automatic scheduling of XMMNewton long term plan,” IWPSS 2009, Pasadena, 2009.
  25. [25] A. K. Jonsson et al., “Planning in Interplanetary Space: Theory and Practice,” Proc. 5th Int. Conf. Artificial Intelligence Planning and Scheduling (AIPS 00), AAAI Press, pp. 177-186, 2000.
  26. [26] S. Chien et al., “An Autonomous Earth-Observing Sensorweb,” IEEE Intelligent Systems, Vol.20, No.3, pp. 16-24, 2005.
  27. [27] G. Verfaillie and M. Lemaitre, “Selecting and scheduling observations for agile satellites: Some lessons from the constraint reasoning community point of view,” in Principles and Practice of Constraint Programming, 7th Int. Conf., CP 2001, Lecture Notes in Computer Science, Vol.2239, T. Walsh, (Ed.) NewYork: Springer-Verlag, pp. 670-684, 2001.
  28. [28] E. Bensana, M. Lemaitre, and G. Verfaillie, “Earth observation satellite management,” Constraints: Int. J., Vol.4, No.3, pp. 293-299, 1999.
  29. [29] M. Ai-Chang et al., “MAPGEN: Mixed-Initiative Planning and Scheduling for the Mars Exploration Rover Mission,” IEEE Intelligent Systems, Vol.19, No.1, pp. 8-12, 2004.

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