Advanced SearchSearch Tips
Simple Pyramid RAM-Based Neural Network Architecture for Localization of Swarm Robots
facebook(new window)  Pirnt(new window) E-mail(new window) Excel Download
 Title & Authors
Simple Pyramid RAM-Based Neural Network Architecture for Localization of Swarm Robots
Nurmaini, Siti; Zarkasi, Ahmad;
  PDF(new window)
The localization of multi-agents, such as people, animals, or robots, is a requirement to accomplish several tasks. Especially in the case of multi-robotic applications, localization is the process for determining the positions of robots and targets in an unknown environment. Many sensors like GPS, lasers, and cameras are utilized in the localization process. However, these sensors produce a large amount of computational resources to process complex algorithms, because the process requires environmental mapping. Currently, combination multi-robots or swarm robots and sensor networks, as mobile sensor nodes have been widely available in indoor and outdoor environments. They allow for a type of efficient global localization that demands a relatively low amount of computational resources and for the independence of specific environmental features. However, the inherent instability in the wireless signal does not allow for it to be directly used for very accurate position estimations and making difficulty associated with conducting the localization processes of swarm robotics system. Furthermore, these swarm systems are usually highly decentralized, which makes it hard to synthesize and access global maps, it can be decrease its flexibility. In this paper, a simple pyramid RAM-based Neural Network architecture is proposed to improve the localization process of mobile sensor nodes in indoor environments. Our approach uses the capabilities of learning and generalization to reduce the effect of incorrect information and increases the accuracy of the agent's position. The results show that by using simple pyramid RAM-base Neural Network approach, produces low computational resources, a fast response for processing every changing in environmental situation and mobile sensor nodes have the ability to finish several tasks especially in localization processes in real time.
Localization Process;RAM-Based Neural Network;Swarm Robots;
 Cited by
B. McElroy, M. Gillham, G. Howells, S. Spurgeon, S. Kelly, J. Batchelor, and M. Pepper, "Highly efficient localisation utilising weightless neural systems," in Proceedings of 2012 European Symposium on Artificial Neural Networks, Bruges, Belgium, 2012, pp. 543-548.

S. Thrun, W. Burgard, and D. Fox, Probabilistic Robotics. Cambridge, MA: MIT Press, 2005.

M. Buehler, K. Iagnemma, and S. Singh, The 2005 DARPA Grand Challenge: The Great Robot Race. Berlin:Springer, 2007.

M. Buehler, K. Iagnemma, and S. Singh, The DARPA Urban Challenge: Autonomous Vehicles in City Traffic. Berlin: Springer, 2009.

H. Lang, Y. Wang, & C. W. De Silva, "Mobile robot localization and object pose estimation using optical encoder, vision and laser sensors," in Proceedings of IEEE International Conference on Automation and Logistics (ICAL2008), Qingdao, China, 2008, pp. 617-622.

A. Napier, G. Sibley, and P. Newman, "Real-time bounded-error pose estimation for road vehicles using vision," in Proceedings of 2010 13th International IEEE Conference on Intelligent Transportation Systems (ITSC), Funchal, Portugal, 2010, pp. 1141-1146.

A. Martinelli, "The odometry error of a mobile robot with a synchronous drive system," IEEE Transactions on Robotics and Automation, vol. 18, no. 3, pp. 399-405, 2002. crossref(new window)

K. S. Chong and L. Kleeman, "Accurate odometry and error modelling for a mobile robot," in Proceedings of 1997 IEEE International Conference on Robotics and Automation, Albuquerque, NM, 1997, pp. 2783-2788.

Y. Sun, J. Xiao, and F. Cabrera-Mora, "Robot localization and energy-efficient wireless communications by multiple antennas," in Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS2009), St. Louis, MO, 2009, pp. 377-381.

M. Conforth and Y. Meng, "An artificial neural network based learning method for mobile robot localization," in Robotics Automation and Control. Vienna: i-TECH, 2008, pp. 103-112.

S. Nurmaini and B. Tutuko, "A new classification technique in mobile robot navigation," Telkomnika, vol. 9, no. 3, pp. 453-464, 2011. crossref(new window)

do Valle Simoes, "An embedded evolutionary controller to navigate a population of autonomous robots," in Frontiers in Evolutionary Robotics. Vienna: i-TECH, 2008, pp. 439-464.

I. Aleksander, M. De Gregorio, F. M. G. Franca, P. M. V. Lima, and H. Morton, "A brief introduction to Weightless Neural Systems," in Proceedings of European Symposium on Artificial Neural Networks (ESANN), Bruges, Belgium, 2009, pp. 299-305.

S. Nurmaini, S. Z. M. Hashim, and D. N. A. Jawawi, "Modular weightless neural network architecture for intelligent navigation," International Journal of Advances in Soft Computing and its Applications, vol. 1, no. 1, pp. 1-18, 2009.

I. Aleksander, W. V. Thomas, and P. A. Bowden, "WISARD: a radical step forward in image recognition," Sensor Review, vol. 4, no. 3, pp. 120-124, 1984. crossref(new window)

W. K. Kan and I. Aleksander, "A probabilistic logic neuron network for associative learning," in Neural Computing Architectures. Cambridge, MA: MIT Press, 1989, pp. 156-171.

I. Aleksander, "From WISARD to MAGNUS: a family of weightless virtual neural machines," in RAM-Based Neural Networks. Singapore: World Scientific, 1998, pp. 18-30.

J. G. Taylor, "Spontaneous behaviour in neural networks," Journal of Theoretical Biology, vol. 36, no. 3, pp. 513-528, 1972. crossref(new window)

R. G. Bowmaker and G. G. Coghili, "Improved recognition capabilities for goal seeking neuron," Electronics Letters, vol. 28, no. 3, pp. 220-221, 1992. crossref(new window)

I. Aleksander, "Ideal neurons for neural computers," in Parallel Processing in Neural Systems and Computers. Amsterdam: Elseriver, 1990, pp. 225-228.

A. F. De Souza, F. Pedroni, E. Oliveira, P. M. Ciarelli, W. F. Henrique, L. Veronese, and C. Badue, "Automated multi-label text categorization with VG-RAM weightless neural networks," Neurocomputing, vol. 72, no. 10, pp. 2209-2217, 2009. crossref(new window)

M. A. Hannan Bin Azhar and K. R. Dimond, "Design of an FPGA based adaptive neural controller for intelligent robot navigation," in Proceedings of Euromicro Symposium on Digital System Design, Dortmund, Germany, 2002, pp. 283-290.

S. S. Botelho, E. do Valle Simoes, L. F. Uebel, and D. Barone, "High speed neural control for robot navigation," in Proceedings of IEEE International Conference on Systems, Man, and Cybernetics, Beijing, China, 1996, pp. 1956-1959.

Q. Yao, D. Beetner, D. C. Wunsch, and B. Osterloh, "A RAM-based neural network for collision avoidance in a mobile robot," in Proceedings of the International Joint Conference on Neural Networks, Portland, OR, 2003, pp. 3157-3160.

B. McElroy and G. Howells, "Automated adaptation of input and output data for a weightless artificial neural network," International Journal of Database Theory and Application, vol. 4, no. 3, pp. 49-58, 2011.

P. Coraggio and M. De Gregorio, "WiSARD and NSP for robot global localization," in Nature Inspired Problem-Solving Methods in Knowledge Engineering. Heidelberg: Springer, 2007, pp. 449-458.

M. De Gregorio, "Active and reactive use of virtual neural sensors, in Proceedings of European Symposium on Artificial Neural Networks (ESANN), Bruges, Belgium, 2008, pp. 349-354.