Rodriguez Brena, Ismael Valentin und Bauer, Adrian Simon und Nottensteiner, Korbinian und Leidner, Daniel und Grunwald, Gerhard und Roa Garzon, Máximo Alejandro (2021) Autonomous Robot Planning System for In-Space Assembly of Reconfigurable Structures. In: 2021 IEEE Aerospace Conference, AERO 2021, Seiten 1-17. IEEE. 2021 IEEE Aerospace Conference (50100), 2021-03-06 - 2021-03-13, Big Sky, MT, USA. doi: 10.1109/AERO50100.2021.9438257. ISBN 978-172817436-5. ISSN 1095-323X.
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Offizielle URL: https://ieeexplore.ieee.org/abstract/document/9438257
Kurzfassung
Large-scale space structures, such as telescopes or spacecrafts, require suitable in-situ assembly technologies in order to overcome the limitations on payload size and mass of current launch vehicles. In many application scenarios, manual assembly by astronauts is either highly cost-inefficient or not feasible at all due to orbital constraints. However, (semi-) autonomous robotic assembly systems may provide the means to construct larger structures in space in the near future. Modularity is a key concept for such structures, and also for reducing costs in novel spacecraft designs. The advantage of the modular approach lies in the capability to generate a high number of unique assets from a reduced number of building blocks. Thus, spacecrafts can be easily adapted to particular use cases, and could even be reconfigured during their lifetime using a robotic manipulation system. These ideas lie at the core of our current EU project MOSAR (MOdular Spacecraft Assembly and Reconfiguration). Teleoperating a space robotic system from Earth to assemble $a$ modular structure is not straightforward. Major difficulties are related to time delays, communication losses, limited control modalities, and low immersion for the operator. Autonomous robotic operations are then preferred, and with this goal we propose $a$ fully autonomous system for planning in-space assembly tasks. Our system is able to generate assembly and reconfiguration plans for modular structures in terms of high-level actions that can autonomously be executed by $a$ robot. Through multiple simulation layers, the system automatically verifies the feasibility and correctness of action sequences created by the planner. The layers implement different levels of abstraction, hierarchically stacked to detect infeasible transitions and initiate replanning at an early stage. Levels of abstraction increase in complexity, ranging from a basic geometric description of the spacecraft, over kinematics of the robotic setup, to full representations of the actions. The system reuses information from failed checks in all layers to avoid similar situations during replanning. We use a hybrid approach where symbolic reasoning is combined with considerations of physical constraints to generate a holistic sequence of actions. We demonstrate our planner for large space structures in a simulation environment. In particular, we consider the reconfiguration of a given modular structure, i.e. disassemble parts and reassemble them in a new configuration. The adaptability of our planning system is shown by executing the assembly plans on robots with different sets of skills and in scenarios with simulated hardware failures.
elib-URL des Eintrags: | https://elib.dlr.de/142864/ | ||||||||||||||||||||||||||||
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Dokumentart: | Konferenzbeitrag (Vortrag) | ||||||||||||||||||||||||||||
Titel: | Autonomous Robot Planning System for In-Space Assembly of Reconfigurable Structures | ||||||||||||||||||||||||||||
Autoren: |
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Datum: | 6 März 2021 | ||||||||||||||||||||||||||||
Erschienen in: | 2021 IEEE Aerospace Conference, AERO 2021 | ||||||||||||||||||||||||||||
Referierte Publikation: | Ja | ||||||||||||||||||||||||||||
Open Access: | Ja | ||||||||||||||||||||||||||||
Gold Open Access: | Nein | ||||||||||||||||||||||||||||
In SCOPUS: | Ja | ||||||||||||||||||||||||||||
In ISI Web of Science: | Ja | ||||||||||||||||||||||||||||
DOI: | 10.1109/AERO50100.2021.9438257 | ||||||||||||||||||||||||||||
Seitenbereich: | Seiten 1-17 | ||||||||||||||||||||||||||||
Verlag: | IEEE | ||||||||||||||||||||||||||||
ISSN: | 1095-323X | ||||||||||||||||||||||||||||
ISBN: | 978-172817436-5 | ||||||||||||||||||||||||||||
Status: | veröffentlicht | ||||||||||||||||||||||||||||
Stichwörter: | Space Robotics, Planning, Failure Detection, Space Assembly, Robotic Assembly, Hybrid Planning | ||||||||||||||||||||||||||||
Veranstaltungstitel: | 2021 IEEE Aerospace Conference (50100) | ||||||||||||||||||||||||||||
Veranstaltungsort: | Big Sky, MT, USA | ||||||||||||||||||||||||||||
Veranstaltungsart: | internationale Konferenz | ||||||||||||||||||||||||||||
Veranstaltungsbeginn: | 6 März 2021 | ||||||||||||||||||||||||||||
Veranstaltungsende: | 13 März 2021 | ||||||||||||||||||||||||||||
Veranstalter : | IEEE | ||||||||||||||||||||||||||||
HGF - Forschungsbereich: | Luftfahrt, Raumfahrt und Verkehr | ||||||||||||||||||||||||||||
HGF - Programm: | Raumfahrt | ||||||||||||||||||||||||||||
HGF - Programmthema: | Robotik | ||||||||||||||||||||||||||||
DLR - Schwerpunkt: | Raumfahrt | ||||||||||||||||||||||||||||
DLR - Forschungsgebiet: | R RO - Robotik | ||||||||||||||||||||||||||||
DLR - Teilgebiet (Projekt, Vorhaben): | R - On-Orbit Servicing [RO], R - Intelligente Mobilität (RM) [RO] | ||||||||||||||||||||||||||||
Standort: | Oberpfaffenhofen | ||||||||||||||||||||||||||||
Institute & Einrichtungen: | Institut für Robotik und Mechatronik (ab 2013) > Autonomie und Fernprogrammierung Institut für Robotik und Mechatronik (ab 2013) > Kognitive Robotik | ||||||||||||||||||||||||||||
Hinterlegt von: | Bauer, Adrian Simon | ||||||||||||||||||||||||||||
Hinterlegt am: | 26 Jul 2021 16:10 | ||||||||||||||||||||||||||||
Letzte Änderung: | 03 Jun 2024 10:31 |
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