Industrial Bin Picking of Potential Entangled Objects in Dense Clutter by Skeletonized Shape Restoration
Author:
Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-63596-0_13
Reference11 articles.
1. Domae, Y., Okuda, H., Taguchi, Y., Sumi, K., Hirai, T.: Fast graspability evaluation on single depth maps for bin picking with general grippers. In:2014 IEEE International Conference on Robotics and Automation (ICRA), pp. 1997–2004. IEEE (2014)
2. Harada, K., Wan, W., Tsuji, T., Kikuchi, K., Nagata, K., Onda, H.: Experiments on learning based industrial bin-picking with iterative visual recognition, arXiv preprintarXiv:1805.08449 (2018)
3. Advances in Intelligent Systems and Computing;R Matsumura,2019
4. Moosmann, M., et al.: Increasing the robustness of random bin picking by avoiding grasps of entangled workpieces. Procedia CIRP 93, 1212–1217 (2020)
5. Moosmann, M., et al.: Transfer learning for machine learning-based detection and separation of entanglements in bin-picking applications. In: 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 1123–1130. IEEE (2022)
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