New Hybrid Fuzzy-CNN Architecture for Human-Robot Interaction
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This article presents the design and the training of a new hybrid network architecture based on architectures of convolutional neural networks and a fuzzy output layer. The architecture is applied to the recognition of objects and it uses the information of distance from the point of capture of the image to the object. It seeks to address the problem of variability in the classification confidence level of a conventional convolutional network by varying the distance of the camera with respect to the object, which is presented in robot-human interaction environments, when the robot should change trajectory, so it does not collide with a person. For the tests carried out, the proposed architecture has a low value of variance and standard deviation with a value of 0.0046 and 0.068, respectively, achieving the task of gripping objects, facing interruptions of their work space, given, for example, by the interaction of a human in it.
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C. Park, J. Kim, J.H. Kang, Robot social skills for enhancing social interaction in physical training, 11th ACM/IEEE International Conference on Human-Robot Interaction (HRI), pp. 493-494, Christchurch, 2016.
R. Jiménez Moreno, L. Brito, Planeación de trayectorias para un móvil robótico en un ambiente 3D, IEEE Biennial Congress of Argentina (ARGENCON), pp. 125-129, 2014.
J. Schmidhuber, Deep learning in neural networks: An overview, Neural Networks, Vol. 61:85–117, 2015.
H. Yan, X. Yu, Y. Zhang, S. Zhanga, X. Zhao, and L. Zhang, Single Image Depth Estimation with Normal Guided Scale Invariant Deep Convolutional Fields, IEEE Transactions on Circuits and Systems for Video Technology, Vol. 26(Issue 1):80-92, January 2019.
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.
A. Caglayan, A.B. Can, 3D convolutional object recognition using volumetric representations of depth data, Fifteenth IAPR International Conference on Machine Vision Applications (MVA), pp. 125-128, 2017.
J. Redmon, A. Angelova, Real-time grasp detection using convolutional neural networks, IEEE International Conference on Robotics and Automation (ICRA), pp. 1316-1322, 2015.
Z. Wang, Z. Li, B. Wang, and H. Liu, Robot grasp detection using multimodal deep convolutional neural networks, Advances in Mechanical Engineering, Vol. 8(Issue 1), September 2016.
X. Chen, and J. Guhl, Industrial Robot Control with Object Recognition based on Deep Learning, Procedia CIRP, Vol. 76:149-154, 2018.
E. Corona, G. Alenyà, A. Gabas, and C. Torras, Active garment recognition and target grasping point detection using deep learning, Pattern Recognition, Vol. 74:629-641, 2018.
Z. Liu, Q. Liu, W. Xu, Z. Liu, Z. Zhou, and J. Chen, Deep Learning-based Human Motion Prediction considering Context Awareness for Human-Robot Collaboration in Manufacturing, Procedia CIRP, Vol. 83:272-278, 2019.
P. R. Futane, R. V. Dharaskar, Video gestures identification and recognition using Fourier descriptor and general fuzzy minmax neural network for subset of Indian sign language, 12th International Conference on Hybrid Intelligent Systems (HIS), pp. 525-530, 2012.
D. Krleža, and K. Fertalj, Graph Matching Using Hierarchical Fuzzy Graph Neural Networks, IEEE Transactions on Fuzzy Systems, Vol. 25(Issue 4):892-904, August 2017.
N. Baklouti, A.M. Alimi, Interval type-2 beta fuzzy neural network for wheeled mobile robots obstacles avoidance, International Conference on Advanced Systems and Electric Technologies (IC_ASET), pp. 481-486. 2017.
Y. Guo, W. Wang, S. Wu, Research on robot path planning based on fuzzy neural network and particle swarm optimization, 29th Chinese Control And Decision Conference (CCDC), pp. 2146-2150, Chongqing, 2017.
Y. Deng, Z. Ren, Y. Kong, F. Bao, and Q. Dai, A Hierarchical Fused Fuzzy Deep Neural Network for Data Classification, IEEE Transactions on Fuzzy Systems, Vol. 25(Issue 4):1006-1012, August 2017.
E. K. Kim, J. Park, J. Y. Kim, S. Kim, Color Decision System Based on Deep Learning and Fuzzy Inference System, Joint 10th International Conference on Soft Computing and Intelligent Systems (SCIS) and 19th International Symposium on Advanced Intelligent Systems (ISIS), pp. 236-239, Toyama, Japan, 2018.
El Kari, B., Ayad, H., El Kari, A., Mjahed, M., Pozna, C., Design and FPGA Implementation of a New Intelligent Behaviors Fusion for Mobile Robot Using Fuzzy Logic, (2019) International Review of Automatic Control (IREACO), 12 (1), pp. 1-10.
Basjaruddin, N., Rakhman, E., Firdaus, R., Kuspriyanto, K., Simple Hand Gesture Recognition Based on Fuzzy Logic for Controlling Mobile Robot, (2019) International Review of Automatic Control (IREACO), 12 (2), pp. 89-94.
T. Nguyen, S. Kavuri, Minho Lee, A multimodal convolutional neuro-fuzzy network for emotion understanding of movie clips, Neural Networks, Vol. 118:208-219, 2019.
J. An, L. Fu, M. Hu, W. Chen, and J. Zhan, A Novel Fuzzy-Based Convolutional Neural Network Method to Traffic Flow Prediction With Uncertain Traffic Accident Information, IEEE Access, Vol. 7:20708-20722, February 2019.
E. P. Ijjina, C.K. Mohan, Human action recognition based on motion capture information using fuzzy convolution neural networks, Eighth International Conference on Advances in Pattern Recognition (ICAPR), pp. 1-6, Kolkata, 2015.
Jimenez-Moreno, R., Martinez, D., A Novel Parallel Convolutional Network Architecture for Depth-Dependent Object Recognition, (2019) International Review of Automatic Control (IREACO), 12 (2), pp. 76-81.
D. C. Ciresan, U. Meier, J. Masci, L.M. Gambardella, J. Schmidhuber, Flexible, high performance convolutional neural networks for image classification, In Twenty-Second International Joint Conference on Artificial Intelligence, pp. 1237-1242, 2011.
B. Robert, Fuzzy and Neural Control DISC Course Lecture notes. Delft University of Technology. September 2004.
César Suescún, Javier Pinzón Arenas, Robinson Jiménez Moreno. Detection of Scratches on Cars by Means of CNN and R-CNN, International Journal on Advanced Science, Engineering and Information Technology, Vol. 9 (2019) No. 3, ISSN: 2088-5334, pages: 745-752.
Pinzon Arenas, J., Jimenez Moreno, R., Hernandez Beleño, R., EMG Signal Acquisition and Processing Application with CNN Testing for MATLAB, (2018) International Review of Automatic Control (IREACO), 11 (1), pp. 44-51.
Useche-Murillo, P., Jimenez-Moreno, R., Pinzon-Arenas, J., Classification of Objects with Occlusions by Means of a DAG-CNN, (2018) International Review of Automatic Control (IREACO), 11 (6), pp. 346-353.
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