Manipulation of Polymorphic Objects Using Two Robotic Arms through CNN Networks
R. J. Moreno, P. C. U. Murillo, R. D. H. Beleño, Algorithm for Tool Grasp Detection, In: Colombia, International Review Of Mechanical Engineering ISSN: 1970-8734 v.12, fasc.1, p.1-8, DOI: https://doi.org/10.15866/ireme.v12i1.12513, (2018).
A. Krizhevsky, I. Sutskever, G. E. Hinton, Imagenet classification with deep convolutional neural networks, In Advances in neural information processing systems, pp. 1097-1105, (2012).
S. Yang, D. Ramanan, Multi-scale recognition with DAG-CNNs, In Computer Vision (ICCV), 2015 IEEE International Conference on, p. 1215-1223, (2015).
C. L. Zitnick, P. Dollár, Edge boxes: Locating object proposals from edges, European Conference on Computer Vision, Springer, Cham, p. 391-405. https://doi.org/10.1007/978-3-319-10602-1_26 (2014).
R. Girshick, J. Donahue, T. Darrell, J. Malik, Rich feature hierarchies for accurate object detection and semantic segmentation, In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 580-587, (2014).
I. Lenz, H. lee, A. Saxena, Deep learning for detecting robotic grasps, The International Journal of Robotics Research, vol. 34, no 4-5, p. 705-724. Doi: 10.1177/0278364914549607, (2015).
J. Redmon, A. Angelova, Real-time grasp detection using convolutional neural networks, En Robotics and Automation (ICRA), 2015 IEEE International Conference on, p. 1316-1322. Doi: 10.1109/ICRA.2015.7139361, (2015).
S. Kumra, C. Kanan, Robotic grasp detection using deep convolutional neural networks, arXiv preprint arXiv: 1611.08036, (2016).
Z. Wang, Z. Li, B. Wang, H. Liu, Robot grasp detection using multimodal deep convolutional neural networks, Advances in Mechanical Engineering, vol. 8, no 9, p. 1687814016668077. Doi: https://doi.org/10.1177/1687814016668077, (2016).
Cornell grasping dataset, http://pr.cs.cornell.edu/grasping/rect data/ data.php, accessed: 2013-09-01.
N. Chen, S. Urban, C. Osendorfer, J. Bayer, P. V. D. Smagt, Estimating finger grip force from an image of the hand using convolutional neural networks and gaussian processes, In Proc. ICRA, (2014).
M. Meier, F Patzelt, R. Haschke, H. J. Ritter, Tactile convolutional networks for online slip and rotation detection, In International Conference on Artificial Neural Networks, Springer, Cham, pp. 12-19, (2016).
S. Levine, P. Pastor, A. Krizhevsky, J. Ibarz, D. Quillen, Learning hand-eye coordination for robotic grasping with deep learning and large-scale data collection, The International Journal of Robotics Research, vol. 37, no 4-5, p. 421-436.
Doi: 10.1177/0278364917710318, (2018).
A. Jin, S. Yeung, J. Jopling, J. Krause, D. Azagury, A. Milstein, L. Fei-Fei, Tool Detection and Operative Skill Assessment in Surgical Videos Using Region-Based Convolutional Neural Networks. arXiv preprint arXiv:1802.08774, (2018).
J. O. P. Arenas, R. J. Moreno, P. C. U. Murillo, Hand Gesture Recognition by Means of Region-Based Convolutional Neural Networks, Contemporary Engineering Sciences, vol. 10, no. 27, pp. 1329-1342, (2017).
J. Wang, J. D. MacKenzie, R. Ramachandran, D. Z. Chen, A deep learning approach for semantic segmentation in histology tissue images, In International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, Cham, pp. 176-184, (2016).
Y. F. Zheng, J. Y. S. LUH, Optimal load distribution for two industrial robots handling a single object, Journal of Dynamic Systems, Measurement, and Control, vol. 111, no 2, p. 232-237, (1989).
J. Y. S. LUH, Y. F. Zheng, Constrained relations between two coordinated industrial robots for motion control, The International journal of robotics research, vol. 6, no 3, p. 60-70, (1987).
D. Giardino, M. Matta, F. Silvestri, S. Spanò, V. Trobiani. FPGA implementation of hand-written number recognition based on CNN, In: International Journal on Advanced Science, Engineering and Information Technology, v. 9, fasc.1, pp. 167-171, (2019).
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