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Paladyn, Journal of Behavioral Robotics

Editor-in-Chief: Schöner, Gregor

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CiteScore 2017: 0.33

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Modular non-computational-connectionist-hybrid neural network approach to robotic systems *

C.D. Bamford / R.J. Mitchell
Published Online: 2012-03-12 | DOI: https://doi.org/10.2478/s13230-012-0003-6


Spiking neural networks are usually limited in their applications due to their complex mathematical models and the lack of intuitive learning algorithms. In this paper, a simpler, novel neural network derived from a leaky integrate and fire neuron model, the ‘cavalcade’ neuron, is presented. A simulation for the neural network has been developed and two basic learning algorithms implemented within the environment. These algorithms successfully learn some basic temporal and instantaneous problems. Inspiration for neural network structures from these experiments are then taken and applied to process sensor information so as to successfully control a mobile robot.

Keywords: neural networks; robotics; spiking neurons; hybrid systems


  • [1] P. Tomei, Adaptive PD controller for robot manipulators, IEEE Transactions on Robotics and Automation, vol. 7, no. 4, (1991), 565–570Google Scholar

  • [2] Bing Hao and Xuefeng Dai, The collision-free motion of robot with Fuzzy neural network, in International Conference on Industrial and Information Systems., (2010), 219–222Google Scholar

  • [3] Dong-Sun Park, Sok Yoon, and YoungBu Kim, Robot end-effector recognition using modular neural network for autonomous control, in International Joint Conference on Neural Networks, (1999), 2032–2036Google Scholar

  • [4] E. T. Rolls, S. M. Stringer, Learning transform invariant object recognition in the visual system with multiple stimuli present during training, Neural Networks, vol. 21, no. 7, (2008), 888–903Google Scholar

  • [5] J. M. Tromans, S. M Stringer, E. T. Rolls, Spatial scene representations formed by self-organizing learning in a hippocampal extension of the ventral visual system, Eur. J. Neurosci., vol. 28, no. 10, (2008), 2116–27Web of ScienceGoogle Scholar

  • [6] Y. S. Xia, Gang Feng, and Jun Wang, A primal-dual neural network for online resolving constrained kinematic redundancy in robot motion control, Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on, vol. 35, no. 1, (2005), 54–64Google Scholar

  • [7] Xiang Yu, S.X. Yang, and M. Ishikawa, Neural network robot controller based on structural learning with forgetting, in IEEE International Symposium on Intelligent Control., (2003), 264–268Google Scholar

  • [8] C. D. Bamford and R. J. Mitchell, Cavacade Neural Network For Mobile Robot, Cybernetic Intelligent Systems (CIS), 2010 IEEE 9th International Conference on, (2010), 1–6Google Scholar

  • [9] D. H., Macready, W. G. Wolpert, No Free Lunch Theorems for Optimization, IEEE Transactions on Evolutionary Computation, vol. 1, no. 1, (1997)Google Scholar

  • [10] Xianyi Yang and M. Meng, A neural network approach to real-time motion planning and control of robot manipulators, in Systems, Man, and Cybernetics, (1999), 674–679Google Scholar

  • [11] S. X. Yang, A biological inspired neural network approach to real-time collision-free motion planning of a nonholonomic car-like robot, in International Conference on Intelligent Robots and Systems IROS, (2000), 239–244Google Scholar

  • [12] F. Alnajjar, I. Bin Mohd Zin, and K. Murase, A Spiking Neural Network with dynamic memory for a real autonomous mobile robot in dynamic environment, in IEEE International Joint Conference on Neural Networks, (2008), 2207–2213Google Scholar

  • [13] R. -J. Wai, Y. -C. Huang, Z. -W. Yang, and C. -Y. Shih, Adaptive fuzzy-neural-network velocity sensorless control for robot manipulator position tracking, Control Theory & Applications, IET, vol. 4, no. 6, (2010), 1079–1093Google Scholar

  • [14] Y. H. Kim and F. L. Lewis, Neural network output feedback control of robot manipulators, in IEEE Transactions on Robotics and Automation, (1999), 301–309Google Scholar

  • [15] M. D. Capuozzo and D. L. Livingston, A compact evolutionary algorithm for integer spiking neural network robot controllers, in Southeastcon, 2011 Proceedings of IEEE, Baltimore, (2011), 237–242Google Scholar

  • [16] F. Alnajjar and K. Murase, Sensor-fusion in spiking neural network that generates autonomous behavior in real mobile robot, in IEEE International Joint Conference on Neural Networks., (2008), 2200–2206Google Scholar

  • [17] A. Schreibman, M. Häusser, M. E. Larkum,I. Segev, and M. London, The information efficacy of a Synapse, Nature Neuroscience, vol. 5, (2002), 332–340Google Scholar

  • [18] W. L. Kath, N. Spruston, Dendritic Arithmetic, Nature Neuroscience, vol. 7, no. 1, (2004),2004 567–569Google Scholar

About the article

Received: 2011-11-11

Accepted: 2012-01-26

Published Online: 2012-03-12

Published in Print: 2011-09-01

Citation Information: Paladyn, Journal of Behavioral Robotics, Volume 2, Issue 3, Pages 126–133, ISSN (Online) 2081-4836, DOI: https://doi.org/10.2478/s13230-012-0003-6.

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© C.D. Bamford et al.. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License. BY-NC-ND 3.0

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