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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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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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CoChat. (2026). Citation check: Utilizing Artificial Intelligence to achieve a robust architecture for future robotic spacecraft. https://app.cochat.ai/papers/10.1109/aero.2015.7119180
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Jaekel, S., & Scholz, B. (2015). Utilizing Artificial Intelligence to achieve a robust architecture for future robotic spacecraft. 2015 IEEE Aerospace Conference. https://doi.org/10.1109/aero.2015.7119180