Learning gait parameters of an ergodic sequential logic CPG model for a hexapod robot

Abstract

In this study, an ergodic sequential logic central pattern generator model was developed to demonstrate the capability of producing various gait patterns. A simple parameter search method enabled the model to acquire typical hexapod gaits such as tripod, tetrapod, and wave gaits. The model was then implemented in a field-programmable gate array (FPGA), and experiments validated that a hexapod robot equipped with the FPGA walked appropriately. It was also shown that the model could be implemented with fewer circuit elements and consumed less power for operation than the conventional CPG model. These results suggest that the model is suitable for gait controllers of neuromorphic robots.