Volume 11, Issue 4 (Journal of Control, V.11, N.4 Winter 2018)                   JoC 2018, 11(4): 13-24 | Back to browse issues page

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Alamiyan harandi F, Derhami V. Feature Extraction from Depth Data using Deep Learning for Supervised Control of a Wheeled Robot . JoC 2018; 11 (4) :13-24
URL: http://joc.kntu.ac.ir/article-1-467-en.html
1- yazd university
Abstract:   (17823 Views)

This paper proposes a framework of Supervised Deep Learning (SDL) for wheeled robot navigation in soft terrains with a focus on wall following and obstacle avoidance tasks. Here, it is supposed the robot is only equipped with a vision system (Kinect camera). The main challenge while using depth images is high dimensionality of images and extracting proper features of them with a purpose of reducing input dimensionality of controller. To do this, the deep learning is utilized in this paper and the appropriate features which are the representation of depth images are acquired. Four architectures are created using this features and the history of steering commands. These architectures are compared in WEBOT simulator. The experiments show that the proposed architecture with four groups of features including: the represented features of depth data, previous represented features, the position of trajectory in color image, and the history of previous steering commands can control the robot in soft terrain with a variety of obstacles as well.  

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Type of Article: Research paper | Subject: Special
Received: 2017/03/29 | Accepted: 2017/08/16 | Published: 2017/11/18

1. Hanafi, D., Abueejela, Y. M., & Zakaria, M. F., 2013, "Wall follower autonomous robot development applying fuzzy incremental controller". Intelligent Control and Automation, 4(1), 18. [DOI:10.4236/ica.2013.41003]
2. Ye, C., Yung, N. H., & Wang, D., 2003, "A fuzzy controller with supervised learning assisted reinforcement learning algorithm for obstacle avoidance". IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 33(1), 17-27. [DOI:10.1109/TSMCB.2003.808179]
3. Fathinezhad, F., Derhami, V., & Rezaeian, M., 2016, "Supervised fuzzy reinforcement learning for robot navigation". Applied Soft Computing, 40, 33-41. [DOI:10.1016/j.asoc.2015.11.030]
4. Fathinezhad, F., & Derhami, V., 2012, "A Novel Supervised Fuzzy Reinforcement Learning for Robot Navigation", [Research]. Journal of Control, 6(3), 1-10.
5. Carelli, R., & Freire, E. O., 2003, "Corridor navigation and wall-following stable control for sonar-based mobile robots". Robotics and Autonomous Systems, 45(3), 235-247. [DOI:10.1016/j.robot.2003.09.005]
6. Karakuş, M. Ö., & Orhan, E., 2013, "Learning of robot navigation tasks by probabilistic neural network". Learning.
7. Zhou, Z., Chen, T., Wu, D., & Yu, C., 2011, "Corridor navigation and obstacle distance estimation for monocular vision mobile robots". JDCTA: Int. J. of Digital Content Technology and its Applications, 5(3), 192-202.
8. Jafar, F. A., Zakaria, N. A., & Yokota, K., 2014, "Visual Features Based Motion Controller for Mobile Robot Navigation". International Journal of Simulation Systems, Science & Technology, 15(1), 7-14.
9. Saeedi, P., Lawrence, P. D., & Lowe, D. G., 2006, "Vision-based 3-D trajectory tracking for unknown environments". IEEE transactions on robotics, 22(1), 119-136. [DOI:10.1109/TRO.2005.858856]
10. Yang, Y., Fu, M., Zhu, H., Xiong, G., & Changsheng, S., 2010, "Control methods of mobile robot rough-terrain trajectory tracking", Control and Automation (ICCA), 2010 8th IEEE International Conference on. Hoffmann, G. M., Tomlin, C. J., Montemerlo, M., & Thrun, S., 2007, "Autonomous automobile trajectory tracking for off-road driving: Controller design, experimental validation and racing", American Control Conference. Oliver, A., Kang, S., Wünsche, B. C., & MacDonald, B., 2012, "Using the Kinect as a navigation sensor for mobile robotics", Proceedings of the 27th Conference on Image and Vision Computing New Zealand. Correa, D. S. O., Sciotti, D. F., Prado, M. G., Sales, D. O., Wolf, D. F., & Osorio, F. S., 2012, "Mobile robots navigation in indoor environments using kinect sensor", Second Brazilian Conference on Critical Embedded Systems (CBSEC).
11. Yung, N. H., & Ye, C., 1999, "An intelligent mobile vehicle navigator based on fuzzy logic and reinforcement learning". IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 29(2), 314-321. [DOI:10.1109/3477.752807]
12. Jang, J., Sun, C., & Mizutani, E., 1997, "Neuro-Fuzzy and Soft Computing, Prentice-Hall, upper Sanddle River".
13. Riedmiller, M., 2005, "Neural fitted Q iteration–first experiences with a data efficient neural reinforcement learning method", European Conference on Machine Learning. [DOI:10.1007/11564096_32]
14. Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A. A., Veness, J., Bellemare, M. G., . . . Ostrovski, G., 2015, "Human-level control through deep reinforcement learning". Nature, 518(7540), 529-533. [DOI:10.1038/nature14236]
15. Lillicrap, T. P., Hunt, J. J., Pritzel, A., Heess, N. M. O., Erez, T., Tassa, Y., . . . Wierstra, D. P., 2016, "Continuous control with deep reinforcement learning": Google Patents.
16. Ondruska, P., & Posner, I., 2016, "Deep tracking: Seeing beyond seeing using recurrent neural networks". The Thirtieth AAAI Conference on Artificial Intelligence (AAAI).
17. Bengio, Y., Courville, A., & Vincent, P., 2013, "Representation learning: A review and new perspectives". IEEE transactions on pattern analysis and machine intelligence, 35(8), 1798-1828. [DOI:10.1109/TPAMI.2013.50]
18. Courville, I. G. a. Y. B. a. A., Deep Learning: MIT Press, 2016.
19. Bengio, Y., 2009, "Learning deep architectures for AI". Foundations and trends® in Machine Learning, 2(1), 1-127.
20. Hinton, G. E., & Salakhutdinov, R. R., 2006, "Reducing the dimensionality of data with neural networks". Science, 313(5786), 504-507. [DOI:10.1126/science.1127647]
21. Lee, H., Grosse, R., Ranganath, R., & Ng, A. Y., 2009, "Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations", Proceedings of the 26th annual international conference on machine learning. [DOI:10.1145/1553374.1553453]
22. Liu, J. N., Hu, Y., You, J. J., & Chan, P. W., 2014, "Deep neural network based feature representation for weather forecasting", Proceedings on the International Conference on Artificial Intelligence (ICAI).
23. Günther, J., Pilarski, P. M., Helfrich, G., Shen, H., & Diepold, K., 2016, "Intelligent laser welding through representation, prediction, and control learning: An architecture with deep neural networks and reinforcement learning". Mechatronics, 34, 1-11. [DOI:10.1016/j.mechatronics.2015.09.004]
24. Williams, D., & Hinton, G., 1986, "Learning representations by back-propagating errors". Nature, 323(6088), 533-538. [DOI:10.1038/323533a0]
25. Vincent, P., Larochelle, H., Lajoie, I., Bengio, Y., & Manzagol, P.-A., 2010, "Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion". Journal of Machine Learning Research, 11(Dec), 3371-3408.

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