Tactile Sensor-based Object Recognition Method Robust to Gripping Conditions Using Fast Fourier Convolution Algorithm |
Huh, Hyunsuk
(Mechanical Engineering, POSTECH)
Kim, Jeong-Jung (KIMM) Koh, Doo-Yoel (KIMM) Kim, Chang-Hyun (KIMM) Lee, Seungchul (Mechanical Engineering, POSTECH) |
1 | K. He, X. Zhang, S. Ren, and J. Sun, "Deep residual learning for image recognition," 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, 2016, DOI: 10.1109/CVPR.2016.90. DOI |
2 | X. Chen and J. Guhl. "Industrial robot control with object recognition based on deep learning," Procedia CIRP, vol. 76, pp. 149-154, 2018, DOI: 10.1016/j.procir.2018.01.021. DOI |
3 | S. Chatterjee, F. H. Zunjani, and G. C. Nandi, "Real-time object detection and recognition on low-compute humanoid robots using deep learning," 2020 6th International Conference on Control, Automation and Robotics (ICCAR), pp. 202-208, Singapore, 2020, DOI: 10.1109/ICCAR49639.2020.9108054. DOI |
4 | Z.-Q. Zhao, P. Zheng, S.-T. Xu, and X. Wu, "Object detection with deep learning: A review," IEEE Transactions on Neural Networks and Learning Systems, vol. 30, no. 11, Nov., 2019, DOI: 10.1109/TNNLS.2018.2876865. DOI |
5 | A. H. Wei and B. Y. Chen, "Robotic object recognition and grasping with a natural background," International Journal of Advanced Robotic Systems, vol. 17, no. 2, 2020, DOI: 10.1177/1729881420921102. DOI |
6 | E. Martinez-Martin and A. P. del Pobil, "Object detection and recognition for assistive robots: Experimentation and implementation," IEEE Robotics & Automation Magazine, vol. 24, no. 3, pp. 123-138, 2017, DOI: 10.1109/MRA.2016.2615329. DOI |
7 | S. Liu, H. Xu, Q. Li, F. Zhang, and K. Hou, "A Robot Object Recognition Method Based on Scene Text Reading in Home Environments," Sensors, vol. 21, no. 5, 2021, DOI: 10.3390/s21051919. DOI |
8 | P. Lang, X. Fu, M. Martorella, J. Dong, R. Qin, X. Meng, and M. Xie, "A comprehensive survey of machine learning applied to radar signal processing," arXiv preprint arXiv:2009.13702, 2020, DOI: 10.48550/arXiv.2009.13702. DOI |
9 | A. Yamaguchi and C. G. Atkeson, "Combining finger vision and optical tactile sensing: Reducing and handling errors while cutting vegetables," 2016 IEEE-RAS 16th International Conference on Humanoid Robots (Humanoids), pp. 1045-1051, Cancun, Mexico, 2016, DOI: 10.1109/HUMANOIDS.2016.7803400. DOI |
10 | J. Shabbir and T. Anwer, "A survey of deep learning techniques for mobile robot applications," arXiv preprint arXiv:1803.07608, 2018, DOI: 10.48550/arXiv.1803.07608. DOI |
11 | J.-J. Kim, D.-Y. Koh, and J. Park, "Obstacle Avoidance for Mobile Robots Using End-to-End Learning," Journal of Institute of Control, Robotics and Systems, vol. 25, no. 6, pp. 541-545, 2019, DOI: 10.5302/J.ICROS.2019.19.0024. DOI |
12 | Pressure Profile Systems, Inc. (PPS), [Online], https://pressureprofile.com, Accessed: March 22, 2022. |
13 | L. Chi, B. Jiang, and Y. Mu, "Fast fourier convolution," Advances in Neural Information Processing Systems, 33, pp. 4479-4488, 2020, [Online], https://papers.nips.cc/paper/2020/file/2fd5d41ec6cfab47e32164d5624269b1-Paper.pdf. |
14 | S. S. A. Zaidi, M. S. Ansari, A. Aslam, N. Kanwal, M. Asghar, and B. Lee, "A survey of modern deep learning based object detection models," Digital Signal Processing, vol. 126, no. 30, June, 2022, DOI: 10.1016/j.dsp.2022.103514. DOI |
15 | Q. Bai, S. Li, J. Yang, Q. Song, Z. Li, and X. Zhang, "Object detection recognition and robot grasping based on machine learning: A survey," IEEE Access, vol. 8, 2020, DOI: 10.1109/ACCESS.2020.3028740. DOI |
16 | M. Zambelli, Y. Aytar, F. Visin, Y. Zhou, and R. Hadsell, "Learning rich touch representations through cross-modal self-supervision," arXiv preprint arXiv:2101.08616, 2021, DOI: 10.48550/arXiv.2101.08616. DOI |
17 | ROBOTIS Co. Ltd., [Online], https://www.robotis.com, Accessed: March 22, 2022. |
18 | G. D. Bergland, "A guided tour of the fast Fourier transform." IEEE Spectrum, vol. 6, no. 7 pp. 41-52, July, 1969, DOI: 10.1109/MSPEC.1969.5213896. DOI |