A Configurable CPG Controller using Connectome based SNN on FPGA for Robot Locomotion

Movement control in autonomous robots requires low-power, real-time models, especially for bio-mimetic locomotion in challenging terrains. Human intervention is often impractical in such environments, making specialized neural networks like central pattern geterators critical for offloading computational resources. This paper presents a controller for a bipedal robot using a modified spiking neuron model, adapted for efficient deployment on field-programmable gate arrays. Key modifications were made to ensure lightweight, real-time performance. Additionally, we leverage a unique open-source neuromorphic software platform for the network design and deployment, making the technology accessible to developers aiming to implement autonomous robot locomotion.