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.