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Cited by 13Space Satellite Systems and ControlSpace Technology and ApplicationsEngineering and Test Systems

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Utilizing Artificial Intelligence to achieve a robust architecture for future robotic spacecraft

Jaekel, Steffen, Scholz, Bastian·2015·2015 IEEE Aerospace Conference
DOI 10.1109/aero.2015.7119180
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Abstract

This paper presents a novel failure-tolerant architecture for future robotic spacecraft. It is based on the Time and Space Partitioning (TSP) principle as well as a combination of Artificial Intelligence (AI) and traditional concepts for system failure detection, isolation and recovery (FDIR). Contrary to classic payload that is separated from the platform, robotic devices attached onto a satellite become an integral part of the spacecraft itself. Hence, the robot needs to be integrated into the overall satellite FDIR concept in order to prevent fatal damage upon hardware or software failure. In addition, complex dexterous manipulators as required for onorbit servicing (OOS) tasks may reach unexpected failure states, where classic FDIR methods reach the edge of their capabilities with respect to successfully detecting and resolving them. Combining, and partly replacing traditional methods with flexible AI approaches aims to yield a control environment that features increased robustness, safety and reliability for space robots. The developed architecture is based on a modular on-board operational framework that features deterministic partition scheduling, an OS abstraction layer and a middleware for standardized inter-component and external communication. The supervisor (SUV) concept is utilized for exception and health management as well as deterministic system control and error management. In addition, a Kohonen self-organizing map (SOM) approach was implemented yielding a real-time robot sensor confidence analysis and failure detection. The SOM features nonsupervized training given a typical set of defined world states. By compiling a set of reviewable three-dimensional maps, alternative strategies in case of a failure can be found, increasing operational robustness. As demonstrator, a satellite simulator was set up featuring a client satellite that is to be captured by a servicing satellite with a 7-DoF dexterous manipulator. The avionics and robot control were integrated on an embedded, space-qualified Airbus e.Cube on-board computer. The experiments showed that the integration of SOM for robot failure detection positively complemented the capabilities of traditional FDIR methods.

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  1. 10.1109/ISKE.2010.5680762 · DOI
  2. 10.1109/SMC-IT.2011.25 · DOI
  3. 10.1016/j.conengprac.2009.04.011 · DOI
  4. falcoz, 2010, Robust Fault Diagnosis for Atmospheric Reentry Vehicles: A Case Study. Systems, Man and Cybernetics, Part A: Systems and Humans, IEEE Transactions on, 40, 886
  5. sturm, 0, Adaptive Body Scheme Models for Robust Robotic Manipulation
  6. 10.1109/TAES.2009.4805259 · DOI
  7. guiotto, 2003, SMART-FDIR: Use of Artificial Intelligence in the Implementation of a Satellite FDIR, Data Systems in Aerospace
  8. ricks, 2009, Methods for Probabilistic Fault Diagnosis Electrical Power System Case Study
  9. 10.1109/IROS.2006.282603 · DOI
  10. holsti, 0, Towards Advanced FDIR Components
  11. 10.1590/S0103-17592008000400004 · DOI
  12. wu, 2010, Fault diagnosis method based on D-S evidence theory, Prognostics and Health Management Conference PHM ‘10
  13. 10.1002/rob.21419 · DOI
  14. 10.2514/6.2009-5978 · DOI
  15. 2010, Space engineering Spacecraft on-board control procedures ECSS-E-ST-70-01C
  16. 10.2514/6.2012-1291059 · DOI
  17. 10.1016/j.paerosci.2012.02.004 · DOI
  18. 10.1016/S0004-3702(98)00068-X · DOI
  19. shafer, 1976, A Mathematical Theory of Evidence, 10.1515/9780691214696 · DOI
  20. 10.1109/IJCNN.1991.155180 · DOI
  21. meyer, 1990, Rocket Engine Failure Detection Using System Identification Techniques In NASA Contractor Report (185259)
  22. 10.1109/IPDPS.2004.1303073 · DOI
  23. 10.1109/DASC.2007.4391842 · DOI
  24. 10.1109/91.531769 · DOI
  25. 2006, AEEC: Avionics application software standard interface, part 1 - required services. ARINC Specication 653P1-2, Part 2 - extended services ARINC Specication 653P2-1 (2008)
  26. kum, 2008, AUTOSAR migration from existing automotive software, International Conference on Control and Automation ICCA
  27. hodson, 2007, Avionics for exploration, NASA Technology Exchange Conference
  28. wander, 2012, Innovative Fault Detection, Isolation and Recovery Strategies On-Board Spacecraft: State of the Art and Research Challenges, German Aerospace Congress
  29. henry, 2010, Fault Detectionand Diagnosis for Aeronautic and Aerospace Missions in Fault tolerant flight control A benchmark challenge, 91, 10.1007/978-3-642-11690-2_3 · DOI
  30. morgan, 2005, Fault protection techniques in JPL Spacecraft, Proceedings of the First International Forum on Integrated System Health Engineering and Management in Aerospace ISHEM
  31. 10.2478/v10006-012-0007-8 · DOI
  32. codetta-raiteri, 2012, ARPHA: a software prototype for fault detection, identification and recovery in autonomous spacecrafts, Acta Futura, 5, 99
  33. 10.1109/IROS.2010.5651121 · DOI
  34. 10.1109/AERO.2012.6187268 · DOI
  35. 10.1109/AERO.2006.1655723 · DOI · Paper page
  36. 10.1163/156855304322758006 · DOI
  37. 10.1109/SMC-IT.2009.11 · DOI
  38. 10.1109/AERO.2014.6836357 · DOI
  39. 10.1109/IROS.2013.6697175 · DOI
  40. rushby, 1999, Partitioning in avionics architectures Requirements mechanisms and assurance NASA Contractor Report CR-1999-209347
  41. eng, 2005, Malay Speech Recognition using Self-Organizing Map and Multilayer Perceptron, Proceedings of the Postgraduate Annual Research Seminar
  42. fusco, 0, European Robotic Arm The Problem of Preventing Collisions
  43. sellmaier, 2010, On-orbit Servicing Missions at DLR/GSOC, 61st International Astronautical Congress
  44. 10.1109/72.846731 · DOI
  45. reppa, 2008, Fault Detection based on Orthotopic Set Membership Identification for Robot Manipulators, Proc 17th IFAC World Congress
  46. 10.1109/TRO.2008.2006705 · DOI
  47. shin, 1999, Fault Detection and Robust Fault Recovery Control for Robot Manipulators with actuator Failures, Proc IEEE Int Conf on Robotics & Automation, 10.1109/ROBOT.1999.772398 · DOI
  48. kohonen, 2001, Self-Organizing Maps Springer Series in Information Sciences, Third Extended Ed, 30

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Metadata

Authors
Jaekel, S., & Scholz, B.
Affiliations
Deutsches Zentrum für Luft- und Raumfahrt e. V. (DLR) (DE)
Venue
2015 IEEE Aerospace Conference
Publisher
IEEE
Published
2015-03
Pages
1-14
ISSN
1095-323X (ISSN-L)
DOI
10.1109/aero.2015.7119180
OpenAlex
W1599016118
Cited by
13
References
1
Citations by year
2024: 2 · 2023: 4 · 2022: 1 · 2021: 1 · 2020: 1 · 2019: 2 · 2017: 1 · 2015: 1
FWCI
1.16
Type
conference paper
Language
en
Open access
closed
Locations
IEEE Aerospace Conference (AERO) (publishedVersion)
Topics
  • Physical Sciences › Engineering › Aerospace Engineering › Space Satellite Systems and Control
  • Physical Sciences › Engineering › Electrical and Electronic Engineering › Space Technology and Applications
  • Physical Sciences › Engineering › Control and Systems Engineering › Engineering and Test Systems
Keywords
robotic spacecraft, space robots, on-orbit servicing, dexterous manipulator, self-organizing maps, fault detection, health management, satellite capture

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