The paper develops a neuromorphic system on a Spartan 6 field programmable gate array (FPGA) board to generate locomotion patterns (gaits) for three different legged robots (biped, quadruped, and hexapod). The neuromorphic system consists of a reconfigurable FPGA-based architecture for a 3G artificial neural network (spiking neural network), which acts as a Central Pattern Generator (CPG). The locomotion patterns, are then generated by the CPG through a general neural architecture, which parameters are offline estimated by means of grammatical evolution and Victor-Purpura distance-based fitness function. The neuromorphic system is fully validated on real biped, quadruped, and hexapod robots.

A bioinspired locomotion system for a quadruped robot is presented. Locomotion is achieved by a spiking neural network (SNN) that acts as a Central Pattern Generator (CPG) producing different locomotion patterns represented by their raster plots. To generate these patterns, the SNN is configured with specific parameters (synaptic weights and topologies), which were estimated by a metaheuristic method based on Christiansen Grammar Evolution (CGE). The system has been implemented and validated on two robot platforms; firstly, we tested our system on a quadruped robot and, secondly, on a hexapod one. In this last one, we simulated the case where two legs of the hexapod were amputated and its locomotion mechanism has been changed. For the quadruped robot, the control is performed by the spiking neural network implemented on an Arduino board with 35% of resource usage. In the hexapod robot, we used Spartan 6 FPGA board with only 3% of resource usage. Numerical results show the effectiveness of the proposed system in both cases.

This paper deals with the design of Spiking Central Pattern Generators (SCPGs) to achieve locomotion at different frequencies in legged robots and their hardware implementation in a FPGA and validation on a real hexapod robot. Herein, the SCPGs are automatically designed by a Christiansen Grammar Evolution (CGE)-based methodology. It is, the CGE performs a solution for the configurations (synaptic weights and connections) of each neuron in the SCPG. This is carried out through the indirect representation of a candidate solution that evolves to replicate a specific spike train according to a locomotion pattern (gait) by measuring the similarity between the spike train with the SPIKE-Distance to drive the search to a correct configuration. By using this evolutionary approach, several SCPG design specifications can be explicitly added into the fitness function to achieve the SPIKE-distance criteria, such as: looking for SNNs with minimal connectivity or a CPG able to generate different locomotion gaits only by changing the initial input stimuli. The SCPG designs have been successfully implemented on a Spartan 6 FPGA board and a real time validation on a 12 DOFs hexapod robot is presented.

This paper presents a grammatical evolution (GE)-based methodology to automatically design third generation artificial neural networks (ANNs), also known as spiking neural networks (SNNs), for solving supervised classification problems. The proposal performs the SNN design by exploring the search space of three-layered feedforward topologies with configured synaptic connections (weights and delays) so that no explicit training is carried out. Besides, the designed SNNs have partial connections between input and hidden layers which may contribute to avoid redundancies and reduce the dimensionality of input feature vectors. The proposal was tested on several well-known benchmark datasets from the UCI repository and statistically compared against a similar design methodology for second generation ANNs and an adapted version of that methodology for SNNs; also, the results of the two methodologies and the proposed one were improved by changing the fitness function in the design process. The proposed methodology shows competitive and consistent results, and the statistical tests support the conclusion that the designs produced by the proposal perform better than those produced by other methodologies.

Effective texture classification requires image descriptors capable of efficiently detecting, extracting and describing the most relevant information in the images, so that, for instance, different texture classes can be distinguished despite image distortions such as varying illuminations, viewpoints, scales, rotations, etc. Designing such an image descriptor is a challenging task that typically involves the intervention of human experts. In this paper, a general method to automatically design effective image descriptors is proposed. Our method is based on Grammatical Evolution and, using a set of example images from a texture classification problem and a classification algorithm as inputs, generates problem-adapted image descriptors that achieve very competitive classification results. Our method is tested on five well-known texture datasets with different number of classes and image distortions to prove its effectiveness and robustness. Our classification results are statistically compared against those obtained by means of six popular hand-crafted texture descriptors in the state-of-the-art. This statistical analysis shows that our evolutionarily designed descriptors outperform most of those designed by human experts.