Abstract
This paper describes a platform for the hardware evolution of Spiking Neural Network (SNN) based robotics controllers on multiple Field Programmable Analogue Arrays (FPAAs). The SNN robotics controller, evolved using a GA, performs obstacle avoidance and navigation. A robotics simulator is used to evaluate the performance of the evolved hardware SNN. Simulated sonar data is input to FPAA neurons and the SNN returns motor control data to the simulator. Initial results indicate the emergence of effective navigation behavior.
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References
Berenson, D., Estévez, N., Lipson, H.: Hardware Evolution of Analog Circuits for In-situ Robotic Fault-Recovery. Submitted to 2005 NASA/DoD Conference on Evolvable Hardware (2005)
Bellis, S., et al.: FPGA Implementation of Spiking Neural Networks - an Initial Step towards Building Tangible Collaborative Autonomous Agents. In: FPT’04, International Conference on Field-Programmable Technology, The University of Queensland, Brisbane, Australia, 6-8 December 2004, pp. 449–452 (2004)
Maass, W.: Networks of spiking neurons: The third generation of neural network models. Neural Networks 10(9), 1659–1671 (1997)
Rocke, P., Maher, J., Morgan, F.: Platform for Intrinsic Evolution of Analogue Neural Networks. In: International Conference on Reconfigurable Computing and FPGAs (ReConFig’05), p. 11 (2005)
Maher, J., et al.: Intrinsic Hardware Evolution of Neural Networks in Reconfigurable Analogue and Digital Devices. In: IEEE Symposium on Field Programmable Custom Computing Machines, FCCM06, Napa, California, USA, April 2006, IEEE Computer Society Press, Los Alamitos (2006)
Terry, M.A., et al.: GRACE: Generative Robust Analog Circuit Design. In: Rothlauf, F., et al. (eds.) EvoWorkshops 2006. LNCS, vol. 3907, pp. 332–343. Springer, Heidelberg (2006)
Upegui, A., Pena-Reyes, C.A., Sanchez, E.: A methodology for evolving spiking neural network topologies on line using partial dynamic reconfiguration. Swiss Federal Institute of Technology, Lausanne, Switzerland
Urzelai, J., Floreano, D.: Evolution of Adaptive Synapses: Robots with Fast Adaptive Behaviour in New Environments. Swiss Federal Institute of Technology, Lausanne, Switzerland
Floreano, D., et al.: Evolutionary Bits’n’Spikes. Swiss Federal Institute of Technology, Lausanne, Switzerland
Floreano, D., Zufferey, J.C., Mattiussi, C.: Evolving Neural Neurons from Wheels to Wings. Swiss Federal Institute of Technology, Lausanne, Switzerland
McGinley, B., Morgan, F.: Evolved Obstacle Avoidance Controller Using a Spiking Neural Network. In: Workshop on Life-Like Perception Systems (March 2005)
MobileSim Robot Simulator: http://robots.mobilerobots.com/MobileSim/
ActivMedia: http://www.activmedia.com
Nolfi, S., et al.: How to Evolve Autonomous Robots: Different Approaches in Evolutionary Robotics. In: 4th International Workshop on Artificial Life (1994)
Haykin, S.: Neural Networks: A Comprehensive Foundation. Macmillan, Basingstoke (1994)
Gerstner, W.: Spiking Neuron Models. Cambridge Univ. Press, Cambridge (2002)
Holland, J.H.: Adaptation in natural and artificial ystem. The University of Michigan Press, Ann Arbor (1975)
Yao, X.: Evolving Artificial Networks. Proceedings of the IEEEÂ 87(9) (1999)
Anadigm Field Programmable Analog Arrays: http://www.anadigm.com
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Rocke, P., McGinley, B., Morgan, F., Maher, J. (2007). Reconfigurable Hardware Evolution Platform for a Spiking Neural Network Robotics Controller. In: Diniz, P.C., Marques, E., Bertels, K., Fernandes, M.M., Cardoso, J.M.P. (eds) Reconfigurable Computing: Architectures, Tools and Applications. ARC 2007. Lecture Notes in Computer Science, vol 4419. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71431-6_36
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DOI: https://doi.org/10.1007/978-3-540-71431-6_36
Publisher Name: Springer, Berlin, Heidelberg
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