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GNNs for Knowledge Transfer in Robotic Assembly Sequence Planning

Atad, Matan (2023) GNNs for Knowledge Transfer in Robotic Assembly Sequence Planning. Masterarbeit, Technische Universität München.

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Kurzfassung

Automated Assembly Sequence Planning (ASP) is a crucial task for robotic systems in manufacturing settings as it increases flexibility and efficiency. However, ASP is still largely performed manually, which can be time-consuming and prone to errors. The ASP process consists of two main steps: determining possible assembly sequences and confirming the feasibility of these sequences with the capabilities and restrictions of the target robot system. While several methods have been developed to automate ASP in recent years, they have limitations such as a lack of flexibility with regard to the properties of the assembly, a long training process, and a dependence on both positive and negative examples for feasibility detection. To address these issues, we propose a graph-based approach that divides the ASP problem into two tasks: Sequence Prediction and Feasibility Prediction. For the Sequence Prediction task, we model assemblies as graphs of part surfaces and apply a Graph Neural Network (GNN) to efficiently extract meaningful information from the input. An expert demonstrator guides us through the sequence prediction process, step-by-step, predicting which parts can be placed in their position at each state of the assembly. Our method is flexible and able to transfer knowledge between different assembly tasks. For Feasibility Prediction, we frame the task as an Anomaly Detection (AD) problem and use our GNN as a feature extractor to create latent representations for each assembly graph. We train a Normalizing Flows (NF) model to model the distribution of feasible assemblies and detect infeasible assemblies as Out-of-Distribution (OoD). Although our method performs better than a baseline one-class classifier, it still lags behind a binary classifier trained on both feasible and infeasible assemblies. However, it is not trivial to obtain a sufficient amount of infeasible assemblies for training. To better understand the limitations of our approach, we conduct ablation studies. Our approach requires only a few seconds to derive a feasible assembly sequence and could be integrated into future ASP frameworks to dramatically reduce planning time while using fewer computational resources compared to previous methods. To our knowledge, we are the first to use NF for graph-level Anomaly Detection. Overall, our method has the potential to significantly improve the efficiency and effectiveness of ASP in manufacturing settings.

elib-URL des Eintrags:https://elib.dlr.de/194212/
Dokumentart:Hochschulschrift (Masterarbeit)
Titel:GNNs for Knowledge Transfer in Robotic Assembly Sequence Planning
Autoren:
AutorenInstitution oder E-Mail-AdresseAutoren-ORCID-iDORCID Put Code
Atad, MatanNICHT SPEZIFIZIERTNICHT SPEZIFIZIERTNICHT SPEZIFIZIERT
Datum:15 Februar 2023
Erschienen in:GNNs for Knowledge Transfer in Robotic Assembly Sequence Planning
Referierte Publikation:Nein
Open Access:Ja
Seitenanzahl:84
Status:veröffentlicht
Stichwörter:Assembly Sequence Planning; Graph Representation; Feasibility Prediction;
Institution:Technische Universität München
Abteilung:Informatics
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 - Autonome, lernende Roboter [RO]
Standort: Oberpfaffenhofen
Institute & Einrichtungen:Institut für Robotik und Mechatronik (ab 2013) > Perzeption und Kognition
Hinterlegt von: Feng, Jianxiang
Hinterlegt am:14 Mär 2023 16:39
Letzte Änderung:15 Jun 2023 07:12

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