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

Jaekel, Steffen and Scholz, Bastian (2015) Utilizing Artificial Intelligence for Achieving a Robust Architecture for Future Robotic Spacecraft. In: IEEE Aerospace Conference. Aerospace Conference, 2015 IEEE, 7-14 March 2015, Big Sky, MT.

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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 - 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.

Item URL in elib:https://elib.dlr.de/100735/
Document Type:Conference or Workshop Item (Speech)
Title:Utilizing Artificial Intelligence for Achieving a Robust Architecture for Future Robotic Spacecraft
Authors:
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Jaekel, Steffensteffen.jaekel (at) dlr.deUNSPECIFIED
Scholz, Bastianbastian.scholz (at) dlr.deUNSPECIFIED
Date:2015
Journal or Publication Title:IEEE Aerospace Conference
Refereed publication:Yes
Open Access:Yes
Gold Open Access:No
In SCOPUS:No
In ISI Web of Science:Yes
Status:Published
Keywords:Artificial Intelligence, On-Orbit Servicing, Robotics
Event Title:Aerospace Conference, 2015 IEEE
Event Location:Big Sky, MT
Event Type:international Conference
Event Dates:7-14 March 2015
Organizer:IEEE
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Space Technology
DLR - Research area:Raumfahrt
DLR - Program:R SY - Technik für Raumfahrtsysteme
DLR - Research theme (Project):R - Vorhaben on Orbit Servicing
Location: Oberpfaffenhofen
Institutes and Institutions:Institute of Robotics and Mechatronics (since 2013) > Autonomy and Teleoperation
Deposited By: Jäkel, Steffen
Deposited On:10 Dec 2015 10:17
Last Modified:31 Jul 2019 19:57

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