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Utilizing Artificial Intelligence for Achieving a Robust Architecture for Future Robotic Spacecraft

Jaekel, Steffen und Scholz, Bastian (2015) Utilizing Artificial Intelligence for Achieving a Robust Architecture for Future Robotic Spacecraft. In: IEEE Aerospace Conference. Aerospace Conference, 2015 IEEE, 2015-03-07 - 2015-03-14, Big Sky, MT.

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Kurzfassung

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 - ntegrated 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.

elib-URL des Eintrags:https://elib.dlr.de/100735/
Dokumentart:Konferenzbeitrag (Vortrag)
Titel:Utilizing Artificial Intelligence for Achieving a Robust Architecture for Future Robotic Spacecraft
Autoren:
AutorenInstitution oder E-Mail-AdresseAutoren-ORCID-iDORCID Put Code
Jaekel, Steffensteffen.jaekel (at) dlr.deNICHT SPEZIFIZIERTNICHT SPEZIFIZIERT
Scholz, Bastianbastian.scholz (at) dlr.deNICHT SPEZIFIZIERTNICHT SPEZIFIZIERT
Datum:2015
Erschienen in:IEEE Aerospace Conference
Referierte Publikation:Ja
Open Access:Ja
Gold Open Access:Nein
In SCOPUS:Nein
In ISI Web of Science:Ja
Status:veröffentlicht
Stichwörter:Artificial Intelligence, On-Orbit Servicing, Robotics
Veranstaltungstitel:Aerospace Conference, 2015 IEEE
Veranstaltungsort:Big Sky, MT
Veranstaltungsart:internationale Konferenz
Veranstaltungsbeginn:7 März 2015
Veranstaltungsende:14 März 2015
Veranstalter :IEEE
HGF - Forschungsbereich:Luftfahrt, Raumfahrt und Verkehr
HGF - Programm:Raumfahrt
HGF - Programmthema:Technik für Raumfahrtsysteme
DLR - Schwerpunkt:Raumfahrt
DLR - Forschungsgebiet:R SY - Technik für Raumfahrtsysteme
DLR - Teilgebiet (Projekt, Vorhaben):R - On-Orbit Servicing [SY]
Standort: Oberpfaffenhofen
Institute & Einrichtungen:Institut für Robotik und Mechatronik (ab 2013) > Autonomie und Fernprogrammierung
Hinterlegt von: Jäkel, Steffen
Hinterlegt am:10 Dez 2015 10:17
Letzte Änderung:24 Apr 2024 20:06

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