A CPG system based on spiking neurons for hexapod robot locomotion

In this paper, we propose a locomotion system based on a central pattern generator (CPG) for a hexapod robot, suitable for embedded hardware implementation. The CPG system was built as a network of spiking neurons, which produce rhythmic signals for three different gaits (walk, jogging and run) in the hexapod robot. The spiking neuron model used in this work is a simplified form of the well-known generalized Integrate-and-Fire neuron model, which can be trained using the Simplex method. The use of spiking neurons makes the system highly suitable for digital hardware implementations that exploit the inherent parallelism to replicate the intrinsic, computationally efficient, distributed control mechanism of CPGs. The system has been implemented on a Spartan 6 FPGA board and fully validated on a hexapod robot. Experimental results show the effectiveness of the proposed approach, based on existing models and techniques, for hexapod rhythmic locomotion.

[1]  M. Saltzman.  Implementing the Simplex Method , 2011 .

[2]  Auke Jan Ijspeert,et al.  Central pattern generators for locomotion control in animals and robots: A review , 2008, Neural Networks.

[3]  Martyna J Grabowska,et al.  Quadrupedal gaits in hexapod animals – inter-leg coordination in free-walking adult stick insects , 2012, Journal of Experimental Biology.

[4]  Jose Hugo Barron-Zambrano,et al.  FPGA implementation of a configurable neuromorphic CPG-based locomotion controller , 2013, Neural Networks.

[5]  Bernard Girau,et al.  The role of the asymptotic dynamics in the design of FPGA-based hardware implementations of gIF-type neural networks , 2011, Journal of Physiology-Paris.

[6]  Robert E. Bixby,et al.  Implementing the Simplex Method: The Initial Basis , 1992, INFORMS J. Comput..

[7]  Anthony N. Burkitt,et al.  A review of the integrate-and-fire neuron model: II. Inhomogeneous synaptic input and network properties , 2006, Biological Cybernetics.

[8]  H Rostro-Gonzalez,et al.  Parameter estimation in spiking neural networks: a reverse-engineering approach , 2012, Journal of neural engineering.

[9]  Peter Dayan,et al.  Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems , 2001 .

[10]  Gert Cauwenberghs,et al.  Neuromorphic Silicon Neuron Circuits , 2011, Front. Neurosci.

[11]  Nicholas T. Carnevale,et al.  Simulation of networks of spiking neurons: A review of tools and strategies , 2006, Journal of Computational Neuroscience.

[12]  E. Marder,et al.  Central pattern generators and the control of rhythmic movements , 2001, Current Biology.

[13]  Anthony N. Burkitt,et al.  A Review of the Integrate-and-fire Neuron Model: I. Homogeneous Synaptic Input , 2006, Biological Cybernetics.

[14]  T. Brown.  On the nature of the fundamental activity of the nervous centres; together with an analysis of the conditioning of rhythmic activity in progression, and a theory of the evolution of function in the nervous system , 1914, The Journal of physiology.

[15]  Hédi Soula,et al.  Spontaneous Dynamics of Asymmetric Random Recurrent Spiking Neural Networks , 2004, Neural Computation.