Forestry Crane Automation using Learning-based Visual Grasping Point Prediction

Harald Gietler*, Christoph Böhm, Stefan Ainetter, Christian Schöffmann, Friedrich Fraundorfer, Stephan Weiss, Hubert Zangl

*Korrespondierende/r Autor/-in für diese Arbeit

Publikation: Beitrag in Buch/Bericht/KonferenzbandBeitrag in einem KonferenzbandBegutachtung

Abstract

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
Originalspracheenglisch
Titel2022 IEEE Sensors Applications Symposium, SAS 2022 - Proceedings
Seiten1 - 6
ISBN (elektronisch)9781665409810
DOIs
PublikationsstatusVeröffentlicht - 1 Aug. 2022
Veranstaltung17th IEEE Sensors Applications Symposium: SAS 2022 - Hybrider Event, Sundsvall, Schweden
Dauer: 1 Aug. 20223 Aug. 2022

Konferenz

Konferenz17th IEEE Sensors Applications Symposium
KurztitelSAS 2022
Land/GebietSchweden
OrtHybrider Event, Sundsvall
Zeitraum1/08/223/08/22

ASJC Scopus subject areas

  • Instrumentierung
  • Elektrotechnik und Elektronik
  • Maschinelles Sehen und Mustererkennung

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