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Research Article Peer ReviewedAt least two reviewers Open Access

Mobil Manipülatörler için Derin Öğrenme Tabanlı Arıza Teşhis Sistemi

1Bilecik Şeyh Edebali Üniversitesi
✉ Corresponding author: Zekican Yılmaz · zekican.yilmaz@tedu.edu.tr
Proceedings Cognitive Models and Artificial Intelligence Conference Volume 15 Pages 22–26 Year 2023
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References

  1. R. Raja, A. Dutta, & B. DasGupta, "Learning framework for inverse kinematics of a highly redundant mobile manipulator", Robotics and Autonomous Systems, vol. 120, p. 103245, 2019 K. Jang, S. Kim, & J. Park, "Reactive self-collision avoidance for a differentially driven mobile manipulator", Sensors, vol. 21, no. 3, p. 890, 2021 Costa, M. A., Wullt, B., Norrlöf, M., & Gunnarsson, S., “Failure detection in robotic arms using statistical modeling, machine learning and hybrid gradient boosting.”, Measurement, 146, 425-436, 2019. I. Sutskever, O. Vinyals, Q. Le, "Sequence to sequence learning with neural networks", In Proceedings of NIPS, pages 3104–3112, 2014. G. Xiaoqing, W. Jidong, "Trajectory planning theory and method of industrial robot," 2011 3rd International Conference on Computer Research and Development, Shanghai, China, pp. 340-343, 2011 T. Wang, L. Zhang and X. Wang, "Fault Detection for Motor Drive Control System of Industrial Robots Using CNN-LSTM-based Observers," in CES Transactions on Electrical Machines and Systems, vol. 7, no. 2, pp. 144-152, June 2023. Han, P., Ellefsen, A. L., Li, G., Holmeset, F. T., & Zhang, H.. Fault detection with LSTM-based variational autoencoder for maritime components. IEEE Sensors Journal, 21(19), 21903-21912, 2021

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Research Contributions (2019+)Peer reviewed by at least two reviewers
Open AccessCC BY 4.0
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