The Technologies for Remote Reconfiguration of Artificial Intelligence of Robotic Systems in Case of Mission or Driving Conditions Change


Article Preview

This report considers the creation of a controller intended for reconfiguring the artificial intelligence of robotic vehicles. The functional structure of hardware-reconfigurable digital module for intellectual control of robotic vehicles is proposed and further interaction between its functional modules and remote support center in different situations requiring reconfiguration is concerned. The procedures of self-check and self-testing of the hardware-reconfigurable digital module for intellectual control of mobile space-based robots are described, which are necessary to ensure reliability of reconfiguration.



Edited by:

Prof. Jong Wan Hu




A. Ignatov et al., "The Technologies for Remote Reconfiguration of Artificial Intelligence of Robotic Systems in Case of Mission or Driving Conditions Change", Applied Mechanics and Materials, Vol. 851, pp. 477-483, 2016

Online since:

August 2016




* - Corresponding Author

[1] R. O. Amos, C. R. Jagath, FPGA Implementations of Neural Networks. Springer, The Netherlands, (2006).

[2] N. Izeboudjen, A. Farah, H. Bessalah, A. Bouridene, N. Chikhi, Towards a Platform for FPGA Implementation of the MLP Based Back Propagation Algorithm, 2007, 497–505.

DOI: 10.1007/978-3-540-73007-1_61

[3] K. Crammer, A. Kulesza, M. Dredze, Adaptive regularization of weight vectors, Mach. Learn. 91(2) (2013) 155–187.

DOI: 10.1007/s10994-013-5327-x

[4] Y. LeCun, L. Bottou, G. B. Orr, K. R. Müller, Efficient Backprop, In G Montavon, G B Orr, and K-R Müller, editors, Neural networks: Tricks of the Trade, Springer, Heidelberg, 2nd edition, (2012).

DOI: 10.1007/978-3-642-35289-8_3

[5] S. Himavathi, D. Anitha, A. Muthuramalingam, Feed forward Neural Network Implementation in FPGA Using Layer Multiplexing for Effective Resource Utilization, Neural Networks, IEEE Transactions-(2007).

DOI: 10.1109/tnn.2007.891626

[6] Thiang, K. Handry, P. Rendy, Artificial Neural Network with Steepest Descent Backpropagation Training Algorithm for Modeling Inverse Kinematics of Manipulator, World Academy of Sciences, Engineering and Technology, (2009).

[7] S. Haykin, Neural Networks, A Comprehensive Foundation, Second Edition. Prentice Hall, (1999).

[8] J. Liu, D. Liang, A survey of FPGA-Based hardware implementation of ANNs, In Neural Networks and Brain, 2005. ICNN&B '05. 2 (2005) 915–918.

DOI: 10.1109/icnnb.2005.1614769

[9] Yu. Bekhtin, P. Krug, A. Lupachev, I. Zhelbakov, V. Graut, The Classifying Algorithms for Train Barriers Recognition on the basis of Image Fusion Methods and Neural Networks, Proc. of the VI International Conference on Internet Technologies and Applications (ITA 15), 2015, ISBN: 978-1-4799-8036-9. DOI: 10. 1109/ITechA. 2015. 7317407.

DOI: 10.1109/itecha.2015.7317407

[10] P. Krug, A. Ostroukh, T. Morozova, Eu. Kashkin, I. Ivanova, Monitoring of Railroad Parts for the Presence of an Objects on the Rails, ARPN J. Eng. Appl. Sci. 10(18) (2015) 7935–7940, ISSN 1819-6608.

Fetching data from Crossref.
This may take some time to load.