Atad, Matan and Feng, Jianxiang and Rodriguez Brena, Ismael Valentin and Durner, Maximilian and Triebel, Rudolph (2023) Efficient and Feasible Robotic Assembly Sequence Planning via Graph Representation Learning. In: 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2023. IEEE. 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2023, 2023, Detroit, IL, USA. doi: 10.1109/IROS55552.2023.10342352. ISBN 978-166549190-7. ISSN 2153-0858.
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Official URL: https://ieeexplore.ieee.org/document/10342352/
Abstract
Automatic Robotic Assembly Sequence Planning (RASP) can significantly improve productivity and resilience in modern manufacturing along with the growing need for greater product customization. One of the main challenges in realizing such automation resides in efficiently finding solutions from a growing number of potential sequences for increasingly complex assemblies. Besides, costly feasibility checks are always required for the robotic system. To address this, we propose a holistic graphical approach including a graph representation called Assembly Graph for product assemblies and a policy archi- tecture, Graph Assembly Processing Network, dubbed GRACE for assembly sequence generation. Secondly, we use GRACE to extract meaningful information from the graph input and predict assembly sequences in a step-by-step manner. In experi- ments, we show that our approach can predict feasible assembly sequences across product variants of aluminum profiles based on data collected in simulation of a dual-armed robotic system. We further demonstrate that our method is capable of detecting infeasible assemblies, substantially alleviating the undesirable impacts from false predictions, and hence facilitating real- world deployment soon. Code and training data are available at https://github.com/DLR-RM/GRACE.
Item URL in elib: | https://elib.dlr.de/195845/ | ||||||||||||||||||||||||
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Document Type: | Conference or Workshop Item (Speech) | ||||||||||||||||||||||||
Title: | Efficient and Feasible Robotic Assembly Sequence Planning via Graph Representation Learning | ||||||||||||||||||||||||
Authors: |
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Date: | 2023 | ||||||||||||||||||||||||
Journal or Publication Title: | 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2023 | ||||||||||||||||||||||||
Refereed publication: | Yes | ||||||||||||||||||||||||
Open Access: | No | ||||||||||||||||||||||||
Gold Open Access: | No | ||||||||||||||||||||||||
In SCOPUS: | Yes | ||||||||||||||||||||||||
In ISI Web of Science: | No | ||||||||||||||||||||||||
DOI: | 10.1109/IROS55552.2023.10342352 | ||||||||||||||||||||||||
Publisher: | IEEE | ||||||||||||||||||||||||
ISSN: | 2153-0858 | ||||||||||||||||||||||||
ISBN: | 978-166549190-7 | ||||||||||||||||||||||||
Status: | Published | ||||||||||||||||||||||||
Keywords: | Graph Neural Networks, Robotic Assembly Sequence Planning | ||||||||||||||||||||||||
Event Title: | 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2023 | ||||||||||||||||||||||||
Event Location: | Detroit, IL, USA | ||||||||||||||||||||||||
Event Type: | international Conference | ||||||||||||||||||||||||
Event Date: | 2023 | ||||||||||||||||||||||||
HGF - Research field: | Aeronautics, Space and Transport | ||||||||||||||||||||||||
HGF - Program: | Space | ||||||||||||||||||||||||
HGF - Program Themes: | Robotics | ||||||||||||||||||||||||
DLR - Research area: | Raumfahrt | ||||||||||||||||||||||||
DLR - Program: | R RO - Robotics | ||||||||||||||||||||||||
DLR - Research theme (Project): | R - Autonomous learning robots [RO] | ||||||||||||||||||||||||
Location: | Oberpfaffenhofen | ||||||||||||||||||||||||
Institutes and Institutions: | Institute of Robotics and Mechatronics (since 2013) > Perception and Cognition | ||||||||||||||||||||||||
Deposited By: | Feng, Jianxiang | ||||||||||||||||||||||||
Deposited On: | 05 Jul 2023 12:50 | ||||||||||||||||||||||||
Last Modified: | 24 Apr 2024 20:56 |
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