# NeuroPod: a real-time neuromorphic spiking CPG applied to robotics

## TL;DR

NeuroPod introduces a real-time neuromorphic spiking neural network-based Central Pattern Generator for hexapod robot locomotion, inspired by insect behavior, demonstrating effective gait generation without delays or balance issues.

## Contribution

This work presents the first real-time neuromorphic CPG implementation on SpiNNaker for robot locomotion, enabling adaptive gait control inspired by biological systems.

## Key findings

- NeuroPod successfully generates multiple gaits in real-time.
- The neuromorphic CPG operates without causing balance loss.
- Real-time measurements match simulation results.

## Abstract

Initially, robots were developed with the aim of making our life easier, carrying out repetitive or dangerous tasks for humans. Although they were able to perform these tasks, the latest generation of robots are being designed to take a step further, by performing more complex tasks that have been carried out by smart animals or humans up to date. To this end, inspiration needs to be taken from biological examples. For instance, insects are able to optimally solve complex environment navigation problems, and many researchers have started to mimic how these insects behave. Recent interest in neuromorphic engineering has motivated us to present a real-time, neuromorphic, spike-based Central Pattern Generator of application in neurorobotics, using an arthropod-like robot. A Spiking Neural Network was designed and implemented on SpiNNaker. The network models a complex, online-change capable Central Pattern Generator which generates three gaits for a hexapod robot locomotion. Reconfigurable hardware was used to manage both the motors of the robot and the real-time communication interface with the Spiking Neural Networks. Real-time measurements confirm the simulation results, and locomotion tests show that NeuroPod can perform the gaits without any balance loss or added delay.