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Efficient and Feasible Robotic Assembly Sequence Planning via Graph Representation Learning

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.

Full text not available from this repository.

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/
Document Type:Conference or Workshop Item (Speech)
Title:Efficient and Feasible Robotic Assembly Sequence Planning via Graph Representation Learning
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iDORCID Put Code
Atad, MatanDLRUNSPECIFIEDUNSPECIFIED
Feng, JianxiangUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Rodriguez Brena, Ismael ValentinUNSPECIFIEDhttps://orcid.org/0000-0002-2310-9186UNSPECIFIED
Durner, MaximilianUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Triebel, RudolphUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
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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