Publikation: Forestry Crane Automation using Learnin...
Stammdaten
Titel: | Forestry Crane Automation using Learning-based Visual Grasping Point Prediction |
Untertitel: | |
Kurzfassung: | This paper presents an approach to automate the log-grasping of a forestry crane. A common hydraulic actuated log-crane is converted into a robotic device by retrofitting it with various sensors yielding perception of internal and environmental states. The approach uses a learning-based visual grasp detection. Once a suitable grasping candidate is determined, the crane starts its kinematic controlled operation. The system’s design process is based on a real-sim-real transfer to avoid possibly harmful, to humans and itself, crane behavior. Firstly, the grasping position prediction network is trained with real-world images. Secondly, an accurate simulation model of the crane, including photo-realistic synthetic images, is established. Note that in simulation, the prediction network trained on real-world data can be used without re-training. The simulation is used to design and verify the crane’s control- and the path planning scheme. In this stage, potentially dangerous maneuvers or insufficient quality of sensory information become visible. Thirdly, the elaborated closed-loop system configuration is transferred to the real-world forestry crane. The pick and place capabilities are verified in simulation as well as experimentally. A comparison shows that simulation and real-world scenarios perform equally well, validating the proposed real-sim-real design procedure. |
Schlagworte: |
Publikationstyp: | Beitrag in Proceedings (Autorenschaft) |
Erscheinungsdatum: | 12.09.2022 (Online) |
Erschienen in: |
2022 IEEE Sensors Applications Symposium (SAS)
2022 IEEE Sensors Applications Symposium (SAS)
(
IEEE;
)
zur Publikation |
Titel der Serie: | - |
Bandnummer: | - |
Erstveröffentlichung: | Ja |
Version: | - |
Seite: | S. 1 - 6 |
Versionen
Keine Version vorhanden |
Erscheinungsdatum: | 12.09.2022 |
ISBN (e-book): | - |
eISSN: | - |
DOI: | http://dx.doi.org/10.1109/SAS54819.2022.9881370 |
Homepage: | https://ieeexplore.ieee.org/document/9881370 |
Open Access |
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AutorInnen
Harald Gietler (intern) |
Christoph Böhm (intern) |
Stefan Ainetter (extern) |
Christian Schöffmann (intern) |
Friedrich Fraundorfer (extern) |
Stephan Michael Weiss (intern) |
Hubert Zangl (intern) |
Zuordnung
Organisation | Adresse | ||||
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Fakultät für Technische Wissenschaften
Institut für Intelligente Systemtechnologien
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AT - 9020 Klagenfurt am Wörthersee |
Kategorisierung
Sachgebiete | |
Forschungscluster | Kein Forschungscluster ausgewählt |
Peer Reviewed |
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Publikationsfokus |
Klassifikationsraster der zugeordneten Organisationseinheiten:
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Arbeitsgruppen |
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Kooperationen
Organisation | Adresse | ||
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Technische Universität Graz
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AT - 8010 Graz |
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