Maseeh Summer Undergraduate Research Experience
Funding Source
Undergraduate Research and Mentoring Program (URMP)
Advisor
Alex Hunt
Document Type
Poster
Publication Date
2024
Abstract
Central Pattern Generators (CPGs) are neural circuits found in the spinal cords of many animals. They are responsible for generating computationally efficient rhythmic outputs used in locomotion, limb manipulation, breathing, and intestinal contraction/relaxation. Traditional robotic control models, such as non-spiking neural networks or Proportional-Integral-Derivative (PID) control systems, although faster to simulate, have limitations in handling adaptive and complex tasks. Spiking CPGs, which mimic real neurons by communicating through discrete spikes, are particularly robust in the presence of environmental noise or changes, resulting in more energy-efficient and dynamic behavior. The aim of this research project is to evaluate the NVIDIA Jetson Orin Nano’s ability to handle spiking CPG networks for real-time, adaptive synthetic muscle control for use in the biomimetic quadruped robot being developed and designed at the AARL.
Citation Details
Restrepo, Daniel, "Exploring Real-time Robotic Control Using Synthetic Neural Systems" (2024). Maseeh Summer Undergraduate Research Experience. 72.