Abstract
Autonomous navigation plays important role in mobile robots. In this paper, a navigation controller based on spiking neural networks (SNNs) for mobile robots is presented. The proposed target-reaching navigation controller, in which the reactive architecture is used, is composed of three sub-controllers: the obstacle-avoidance controller and the wall-following controller using spiking neural networks (SNNs), and the goal-reaching controller. The experimental results show that the navigation controller can control the mobile robot to reach the target successfully while avoiding the obstacle and following the wall to get rid of the deadlock caused by local minimum. The proposed navigation controller does not require accurate mathematical models of the environment, and is suitable to unknown and unstructured environment.
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References
Pomerleau, D.A.: Neural Network Perception for Mobile Robot Guidance. Kluwer Academic Publishers, Norwell (1993)
Na, Y.K., Oh, S.Y.: Hybrid Control for Autonomous Mobile Robot Navigation Using Neural Network Based Behavior Modules and Environment Classification. Autonomous Robots 15(2), 193–206 (2003)
Ye, J.: Adaptive Control of Nonlinear PID-based Analog Neural Networks for a Nonholonomic Mobile Robot. Neurocomputing 71, 1561–1565 (2008)
Rossomando, F.G., Soria, C., Carelli, R.: Autonomous Mobile Robots Navigation Using RBF Neural Compensator. Control Engineering Practice 19, 215–222 (2011)
Mohareri, O., Dhaouadi, R., Rad, A.B.: Indirect Adaptive Tracking Control of a Nonholonomic Mobile Robot via Neural Networks. Neurocomputing 88, 54–66 (2012)
Wang, X.Q., Hou, Z.-G., Tan, M., Cheng, L.: A Behavior Controller for Mobile Robot Based on Spiking Neural Networks. Neurocomputing 71, 655–666 (2008)
Wang, X.Q., Hou, Z.-G., Tan, M., Wang, Y.: The Wall-Following Controller for the Mobile Robot Using Spiking Neurons. In: IEEE 2009 ICAICI, pp. 194–199. IEEE Press, New York (2009)
Vreeken J.: Spiking Neural Networks, An Introduction. Technical Report UU-CS-2003-008, Institute for Information and Computing Sciences, Utrecht University (2002), http://ailab.cs.uu.nl/pubs/SNNVreekenIntroduction.pdf
Maass, W., Bishop, C.M. (eds.): Pulsed Neural Networks. MIT-Press, Cambridge (1999)
Schliebs, S., Defoin-Platel, M., Worner, S., Kasabov, N.: Integrated Feature and Parameter Optimization for Evolving Spiking Neural Networks: Exploring Heterogeneous Probabilistic Models. Neural Networks 22(5-6), 623–632 (2009)
Wysoski, S., Benuskova, L., Kasabov, N.: Fast and adaptive network of Spiking neurons for multi-view visual pattern recognition. Neurocomputing 71(13-15), 2563–2575 (2008)
Kasabov, N.: Integrative Connectionist Learning Systems Inspired by Nature: Current Models, Future Trends and Challenges. Natural Computing 8(2), 199–218 (2009)
Kasabov, N.: To Spike or Not to Spike: A Probabilistic Spiking Neuron Model. Neural Networks 23(1), 16–19 (2010)
Kasabov, N.: Evolving Spiking Neural Networks and Neurogenetic Systems for Spatio- and Spectro-Temporal Data Modelling and Pattern Recognition. In: Liu, J., Alippi, C., Bouchon-Meunier, B., Greenwood, G.W., Abbass, H.A. (eds.) WCCI 2012. LNCS, vol. 7311, pp. 234–260. Springer, Heidelberg (2012)
Wang, X.Q., Hou, Z.G., Tan, M., Wang, Y.: Corridor-Scene Classification for Mobile Robot Using Spiking Neurons. In: IEEE Fourth International Conference on Natural Computation, vol. 4, pp. 125–129. IEEE Press, New York (2008)
Wang, X.Q., Hou, Z.G., Tan, M.: Improved Mobile Robot’s Corridor-Scene Classifier Based on Probabilistic Spiking Neuron Model. In: IEEE International Conference on Cognitive Informatics and Cognitive Computing, pp. 348–355. IEEE Press, New York (2011)
Floreano, D., Mattiussi, C.: Evolution of Spiking Neural Controllers for Autonomous Vision-Based Robots. In: Gomi, T. (ed.) ER-EvoRob 2001. LNCS, vol. 2217, pp. 38–61. Springer, Heidelberg (2001)
Floreano, D., Zufferey, J.C., Nicoud, J.D.: From Wheels to Wings with Evolutionary Spiking Neurons. Artificial Life 11, 121–138 (2005)
Hagras, H., Pounds-Cornish, A., Colley, M.: Evolving Spiking Neural Network Controllers for Autonomous Robots. In: IEEE International Conference on Robotics & Automation 2004, pp. 4620–4626. IEEE Press, New York (2004)
Soula, H., Alwan, A., Beslon, G.: Learning at the Edge of Chaos: Temporal Coupling of Spiking Neurons Controller for Autonomous Robotic. In: American Association for Artificial Intelligence (AAAI) Spring Symposia on Developmental Robotic 2005, p. 6 (2005)
Alamdari, R.S.A.: Unknown Environment Representation for Mobile Robot Using Spiking Neural Networks. Transactions on Engineering, Computing and Technology 6, 49–52 (2005)
Wang, X.Q.: Research on Eenvironment Perception and Behavior Control for Mobile Robots Based on Spiking Neural Networks. Ph.D. Thesis, Chinese Academy of Sciences, China (2007)
Arkin, R.C.: Integrating Behavioral, Perceptual, and World Knowledge in Reactive Navigation. Robotics and Autonomous Systems 6(1-2), 105 (1990)
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Wang, X., Hou, ZG., Lv, F., Tan, M., Wang, Y. (2012). A Target-Reaching Controller for Mobile Robots Using Spiking Neural Networks. In: Huang, T., Zeng, Z., Li, C., Leung, C.S. (eds) Neural Information Processing. ICONIP 2012. Lecture Notes in Computer Science, vol 7666. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34478-7_79
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DOI: https://doi.org/10.1007/978-3-642-34478-7_79
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-34477-0
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