Autonomic Computing Software for Autonomous Space Vehicles

Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 144)

Abstract

Current space missions increasingly demand more autonomy in control architectures for Unmanned Space Vehicles (USVs), so unmanned long-term missions can be afforded. Continuous assurance of effective adaptation to unpredictable internal and external changes, along with efficient management of resources is essential for such requirements. One of the attractive solutions is that inspired by the physiology of living systems, where self-regulation helps to achieve continuous adaptation to the environment by changing internal conditions. The physiological functions are performed by nervous system reflexes that are the foundations for self-regulatory mechanisms such as homeostasis. Building artificial self-regulation similar to biological ones into USVs makes them highly-viable and ultra-stable in order to support very long missions. This paper presents aspects of how to endow USVs with Artificial Nervous Reflexes (ANRs) by means of applying physiological principles of self-regulation to the USV’s control architecture, so resilience and persistence can be supported. A case study of a composite orbiter is presented. The studied ANRs are needed to guarantee the self-regulation of response time (latency), operation temperature (thermoregulation), and power consumption (energy balance). Results from a cross-checked analysis of the above self-regulation mechanisms are also presented.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Benkhoff, J.: BepiColombo: Overview and Latest Updates, European Planetary Science Congress, p. 7. EPSC Abstracts (2012)Google Scholar
  2. 2.
    Waugh, A., Grant, A.: Anatomy and Physiology in Health and Illness. Ross and Wilson (2004)Google Scholar
  3. 3.
    Horn, P.: Autonomic Computing: IBM’s perspective on the state of information technology. IBM Research Report (2001)Google Scholar
  4. 4.
    Melchior, N.A., Smart, W.D.: Autonomic systems for mobile robots. In: Proceedings of the 2004 International Conference on AC, New York, USA (2004)Google Scholar
  5. 5.
    Schmeck, H.: Organic computing – a new vision for distributed embedded systems. In: Proceedings of the 8th IEEE International Symposium on Object-Oriented Real-Time Distributed Computing. IEEE Computer Society (2005)Google Scholar
  6. 6.
    Kishi, T.: Model driven design and organic computing: from the viewpoint of application production. In: Proceedings of the IEEE International Symposium on ISORC 2009, pp. 97–98. IEEE Computer Society (2009)Google Scholar
  7. 7.
    Beer, S.: Brain of the Firm. 2nd ed. Wiley (1994)Google Scholar
  8. 8.
    Hilton, J., Wirght, C., Kiparoglou, V.: Building resilience into systems. In: Proceedings of the International Systems Conference, Vancouver, Canada (2012)Google Scholar
  9. 9.
    Ashby, W.R.: The William Ross Ashby Digital Archive (2014). http://www.rossashby.info/index.html
  10. 10.
    Cariani, P.A.: The Homeostat as Embodiment of Adaptive Control. International Journal of General Systems 38(2) (2008)Google Scholar
  11. 11.
    Vassev, E.: ASSL: Autonomic System Specification Language - A Framework for Specification and Code Generation of Autonomic Systems. LAP Lambert Academic Publishing, Germany (2009)Google Scholar
  12. 12.
    Vassev, E.: Towards a Framework for Specification and Code Generation of Autonomic Systems. Ph.D. Thesis, Department of Computer Science and Software Engineering, Concordia University, Montreal, Canada (2008)Google Scholar
  13. 13.
    Insaurralde, C.C., Vassev, E.: Software specification and automatic code generation to realize homeostatic adaptation in unmanned spacecraft. In: Proceedings of the International C* Conference on Computer Science and Software Engineering (C3S2E 2014), pp. 35–44. ACM (2014)Google Scholar
  14. 14.
    Vassev, E., Hinchey, M., Montanari, U., Bicocchi, N., Zambonelli, F., Wirsing, M.: D3.2: Second Report on WP3: The KnowLang Framework for Knowledge Modeling for SCE Systems. ASCENS Project Deliverable (2012)Google Scholar

Copyright information

© Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2015

Authors and Affiliations

  1. 1.Institute of Sensors, Signals and SystemsHeriot-Watt UniversityEdinburghUK
  2. 2.Lero–the Irish Software Engineering Research CentreUniversity of LimerickLimerickIreland

Personalised recommendations