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
Original language | English |
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Title of host publication | 2022 IEEE Sensors Applications Symposium, SAS 2022 - Proceedings |
Pages | 1 - 6 |
ISBN (Electronic) | 9781665409810 |
DOIs | |
Publication status | Published - 1 Aug 2022 |
Event | 17th IEEE Sensors Applications Symposium: SAS 2022 - Hybrider Event, Sundsvall, Sweden Duration: 1 Aug 2022 → 3 Aug 2022 |
Conference
Conference | 17th IEEE Sensors Applications Symposium |
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Abbreviated title | SAS 2022 |
Country/Territory | Sweden |
City | Hybrider Event, Sundsvall |
Period | 1/08/22 → 3/08/22 |
ASJC Scopus subject areas
- Instrumentation
- Electrical and Electronic Engineering
- Computer Vision and Pattern Recognition